Journal of Manufacturing and Materials Processing

32 pages, 7900 KB

Open AccessEditor’s ChoiceArticle

Smart Manufacturing Scheduling Under Data Latency: A Rolling-Horizon Two-Stage MILP Framework for OEM–Tier-1 Coordination

by Harshkumar K. Parmar and Shivakumar Raman

J. Manuf. Mater. Process. 2026, 10(4), 142; https://doi.org/10.3390/jmmp10040142 - 21 Apr 2026

Cited by 1 | Viewed by 2236

Abstract

Real-time coordination across OEM–Tier-1 manufacturing networks remains challenging due to delayed shop-floor data, stochastic machine availability, and the need for schedule stability. This paper presents a protocol-agnostic, two-stage mixed-integer linear programming (MILP) framework for real-time family-level scheduling. The method integrates MTConnect-like data streams [...] Read more.

Real-time coordination across OEM–Tier-1 manufacturing networks remains challenging due to delayed shop-floor data, stochastic machine availability, and the need for schedule stability. This paper presents a protocol-agnostic, two-stage mixed-integer linear programming (MILP) framework for real-time family-level scheduling. The method integrates MTConnect-like data streams without requiring adherence to any single communication standard. In Stage 1, a baseline plan is generated using expected capacity; in Stage 2, a rolling-horizon recourse model adapts the plan to observed (possibly lagged) capacity while incorporating a stability penalty to control resequencing. A synthetic OEM–Tier-1 testbed with three machines (two Tier-1, one OEM) is used to benchmark performance under real-time (L = 0) and delayed (L = 5) data scenarios. Across these scenarios, the real-time rolling scheduler improves strict on-time fulfillment by approximately 70% and eliminates terminal backlog relative to static planning, while MILP solve times remain under 0.1 s per cycle. Sensitivity experiments that vary disruption intensity, replanning interval (Δ), and stability weight (λ) show consistent qualitative trends and illustrate how the framework can be tuned to balance service performance against schedule stability without sacrificing computational tractability. Full article

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13 pages, 4752 KB

Open AccessEditor’s ChoiceArticle

Advancing Sustainable Manufacturing for Cutting Tools: The Role of Green Machining and Tool Regrinding

by Berend Denkena, Benjamin Bergmann, Thomas Geschwind and Lars Luthe

J. Manuf. Mater. Process. 2026, 10(4), 140; https://doi.org/10.3390/jmmp10040140 - 21 Apr 2026

Viewed by 1495

Abstract

High-performance cutting materials are central to modern production engineering. Cemented carbides dominate industrial tooling, while polycrystalline boron nitride (PcBN) is established for hard turning and finishing nickel-based alloys. The associated tool manufacturing chains are energy- and effort-intensive, motivating approaches that reduce material losses [...] Read more.

High-performance cutting materials are central to modern production engineering. Cemented carbides dominate industrial tooling, while polycrystalline boron nitride (PcBN) is established for hard turning and finishing nickel-based alloys. The associated tool manufacturing chains are energy- and effort-intensive, motivating approaches that reduce material losses and primary energy demand. This study quantifies energy consumption across the production of solid carbide cutting tools with a focus on near-net-shape green machining, its impact on subsequent grinding and material recirculation. It also quantifies energy consumption for regrinding PcBN cutting tools. Power measurements were recorded during green machining and tool grinding of cylindrical versus pre-contoured (green-machined) blanks, including coolant units for the carbide tools during operation. Tool performance of the carbide tools was assessed via milling tests in 42CrMo4; PcBN reground tools were evaluated in Inconel 718. In the process chain of carbide tool production, specific energy decreased from 6.98 to 6.36 kWh/kg (−8.88%) despite +0.461 kWh/kg for green machining; direct recirculation of green-machined material saved an additional 5.861 kWh/kg. Reground PcBN inserts achieved comparable tool life to new tools while reducing energy by ≈85% per insert. The dominant levers for energy reduction are shorter grinding times in the presence of high machine and coolant base loads and systematic regrinding of high-embodied-energy tools. Full article

(This article belongs to the Special Issue Advanced and Sustainable Machining)

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31 pages, 24349 KB

Open AccessEditor’s ChoiceArticle

Geometry-Aware Neural Network for Generalized Temperature Prediction in Microwave Heating of PET Preforms

by Ahmad Alsheikh and Andreas Fischer

J. Manuf. Mater. Process. 2026, 10(4), 138; https://doi.org/10.3390/jmmp10040138 - 19 Apr 2026

Viewed by 885

Abstract

Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. [...] Read more.

Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. Conventional neural network models trained on specific geometric configurations typically fail to generalize to unseen preform designs, requiring costly retraining for each new geometry. This work proposes a unified geometry-aware deep learning framework that predicts spatial temperature distributions across multiple preform designs using a single neural network model. The approach reformulates temperature prediction as a coordinate-level regression task conditioned on spatial location, geometric descriptors, process parameters, and structural region labels. A domain-bounded training strategy based on extreme feasible preform geometries is introduced, ensuring that predictions for intermediate designs remain within the interpolation regime of the network. The framework is evaluated on six distinct preform geometries, demonstrating that a single model can generalize reliably to new, unseen preform designs when their geometric parameters fall within the bounds of the training data. This is achieved through a domain-bounded training strategy that constructs datasets from the extreme feasible geometries, thereby converting the prediction of any intermediate design into an interpolation task. Since neural networks are inherently limited in their ability to extrapolate beyond the training domain, this formulation is essential for ensuring stable and accurate predictions across the full range of industrially relevant preform configurations. The proposed methodology provides a foundation for geometry-informed surrogate modeling in thermal process control and can be extended to other manufacturing systems characterized by strong geometric variability. Full article

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28 pages, 2852 KB

Open AccessEditor’s ChoiceArticle

Defect Monitoring of Complex Geometries Through Machine Learning in LPBF Metal Additive Manufacturing

by Marcin Magolon, Jan Boer and Mohamed Elbestawi

J. Manuf. Mater. Process. 2026, 10(4), 127; https://doi.org/10.3390/jmmp10040127 - 9 Apr 2026

Cited by 2 | Viewed by 1734

Abstract

Laser powder bed fusion (LPBF) can fabricate intricate metal components but is prone to defects, such as porosity and cracks, that degrade performance. We present an in situ monitoring framework that fuses structure-borne acoustic emission (AE) and coaxial two-color pyrometry acquired synchronously at [...] Read more.

Laser powder bed fusion (LPBF) can fabricate intricate metal components but is prone to defects, such as porosity and cracks, that degrade performance. We present an in situ monitoring framework that fuses structure-borne acoustic emission (AE) and coaxial two-color pyrometry acquired synchronously at 1 MHz. Modality-specific encoders are pretrained separately, their latent representations are exported, and a lightweight feature-level fusion classifier with two binary heads predicts crack-like and porosity-like indications. Evaluation uses a held-out grouped experiment/build-machine-part split with independent Archimedes density and micro-CT ground truth. On the held-out test set, the fused model achieved F1 = 0.974 for crack-like detection and F1 = 0.987 for porosity-like detection, with AUROC = 0.998 and 0.993, respectively. Recall was 1.00 for both heads, corresponding to false-positive rates of 11.18% for crack-like and 0.945% for porosity-like indications. These results support synchronized AE-pyrometry fusion as a promising high-sensitivity in situ screening approach for LPBF. A later matched within-framework ablation campaign was also performed under stricter checkpoint-screening rules to compare AE + PY + Aux, AE + PY, AE-only, and PY-only variants under a common grouped-split protocol. Together, these results support multimodal monitoring while highlighting the need for explicit coupon/geometry-stratified reporting and for separately architecture-optimized unimodal baselines. Full article

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32 pages, 9104 KB

Open AccessEditor’s ChoiceArticle

Validation and Generalization of Key Building Blocks for Cyber-Physical Systems in Manufacturing: Insights from Automotive Inspection and Assembly Use Cases

by Michael Gfoellner, Christoph Kribernegg, Stefan Koerner, Martin Schellander and Franz Haas

J. Manuf. Mater. Process. 2026, 10(4), 116; https://doi.org/10.3390/jmmp10040116 - 29 Mar 2026

Viewed by 1094

Abstract

A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between [...] Read more.

A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between different products, while ensuring consistent geometric alignment throughout the process. Geometrical deviations in these devices can adversely impact product quality along the entire vehicle assembly chain. This paper presents the development and implementation of two patented use cases: a cyber-physical inspection system, fully operational in serial production, and a cyber-physical assembly system, tested successfully in the prototype phase. The first actively mitigates the effects of device deviations in real time, while the second enables the on-demand configuration of flexible, advanced positioning devices via precision part matching, effectively preventing systematic deviations. Challenges and insights from both systems are discussed. Four previously introduced building blocks for automating quality control processes are validated and generalized for broad applicability across manufacturing processes and project phases via cross-system comparative analysis: the integrated capture of process and product data, automated data analytics, automated decision-making, and autonomous process intervention. This work proposes a validated, scalable framework integrating the design and implementation of cyber-physical systems to support zero-defect manufacturing. Full article

(This article belongs to the Special Issue Emerging Trends in Robotics and Automation for Advanced Manufacturing)

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17 pages, 1788 KB

Open AccessEditor’s ChoiceArticle

Geometry-Dependent Mechanical Performance of Additively Manufactured Metal–Polymer Hybrid Joints with Lattice-Based Transition Zones

by Alexander Walzl and Konstantin Prabitz

J. Manuf. Mater. Process. 2026, 10(3), 103; https://doi.org/10.3390/jmmp10030103 - 17 Mar 2026

Viewed by 887

Abstract

Metal–polymer hybrid joints are gaining importance as they combine high structural rigidity with a low weight. Additive manufacturing processes such as the laser powder bed fusion process (L-PBF) enable the production of complex metallic lattice structures that allow for form-fitting force transmission between [...] Read more.

Metal–polymer hybrid joints are gaining importance as they combine high structural rigidity with a low weight. Additive manufacturing processes such as the laser powder bed fusion process (L-PBF) enable the production of complex metallic lattice structures that allow for form-fitting force transmission between the metal and polymer as mechanical interlock elements. In this work, metal–polymer hybrid compounds with additively manufactured transition zones are systematically investigated and mechanically evaluated. Three different lattice geometries (z4A, z8A, z8V) were fabricated from maraging steel (1.2709) using L-PBF and then hybridised with injection moulding using polypropylene (PP C7069-100NA). Mechanical characterisation was performed by tensile tests according to DIN EN ISO 527, in combination with statistical analyses and an analytical serial three-spring model to determine the homogenised elasticity modulus of the transition zone. The results show significant geometry-related differences in tensile strength, maximum force, and effective stiffness. The A-shaped transition zone geometry (z4A) achieves the highest mechanical performance and up to 82% of the tensile strength of the pure polymer, while the V-shaped transition zone geometry (z8V) has significantly lower load-bearing capacities. Variance analysis shows a dominant geometric influence with effect strength of η² ≈ 0.99. The analytically predicted stiffness values match the experimental results within 5–10%. This work demonstrates a reproducible, simulation-sparse approach to the analysis and design of metal–polymer hybrid connections. Full article

(This article belongs to the Special Issue Advancements in Metal Additive Manufacturing: Technologies and Applications)

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36 pages, 1628 KB

Open AccessEditor’s ChoiceReview

Degradation and Long-Term Response Evaluation of Polymeric Components Produced by Additive Manufacturing

by Claudia Solek, Jorge Crespo-Sánchez, Sergio Fuentes del Toro, Jorge Ayllón, Mariaenrica Frigione, Ana María Camacho, Juan Rodríguez-Hernández and Alvaro Rodríguez-Prieto

J. Manuf. Mater. Process. 2026, 10(3), 102; https://doi.org/10.3390/jmmp10030102 - 17 Mar 2026

Cited by 4 | Viewed by 3080

Abstract

Additive manufacturing (AM) has rapidly evolved from a prototyping tool into an effective method for producing end-use components, thanks to its ability to produce complex, lightweight and customised parts. However, this technique requires a thorough understanding of the long-term behaviour and degradation mechanisms [...] Read more.

Additive manufacturing (AM) has rapidly evolved from a prototyping tool into an effective method for producing end-use components, thanks to its ability to produce complex, lightweight and customised parts. However, this technique requires a thorough understanding of the long-term behaviour and degradation mechanisms of components, especially when polymers are involved in the printing process. Unlike polymer components manufactured using traditional methods, polymers produced through AM exhibit unique microstructures, anisotropies, and interfacial characteristics due to the layer-by-layer fabrication process. These features can affect how these materials respond to thermal, mechanical and environmental stresses over time. Furthermore, technology-specific processing parameters directly govern porosity distribution, crystallinity evolution, interlayer bonding quality, and residual stress development, all of which are key factors for ensuring long-term performance. This review aims to support researchers in the development of durable additively manufactured polymer components by systematically analysing polymer degradation mechanisms, accelerated ageing and lifetime prediction methodologies. Following a PRISMA-based screening process, approximately 160 international standards relevant to polymer durability in additive manufacturing were selected from an initial corpus of about 620 documents for in-depth analysis. Processing–structure–property relationships specific to the AM processing of polymers, including the commonly used FFF (fused filament fabrication), SLA (stereolithography) and SLS (selective laser sintering), are examined in relation to crucial aspects for long-term structural integrity and degradation behaviour. Finally, limitations within the current normative framework are identified, emphasising the absence of process-aware durability assessment protocols and the need for dedicated standards tailored to additively manufactured polymer components. Full article

(This article belongs to the Special Issue Advances in 3D Printing Technologies: Materials, Processes, and Applications, 2nd Edition)

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19 pages, 5603 KB

Open AccessEditor’s ChoiceArticle

The Influence of Heat and Holding Time on the Warm Forming of Al–Mg–Si Alloys

by Vasco Simões, Marta Oliveira, Hervé Laurent and Luis Menezes

J. Manuf. Mater. Process. 2026, 10(3), 94; https://doi.org/10.3390/jmmp10030094 - 11 Mar 2026

Cited by 3 | Viewed by 1604

Abstract

Warm forming of heat-treatable aluminium alloys can induce significant changes in their initial heat treatment, affecting both the forming process and the final in-service properties. This work aims to systematically investigate the influence of heat-holding time on the thermo-mechanical behaviour and post-forming properties [...] Read more.

Warm forming of heat-treatable aluminium alloys can induce significant changes in their initial heat treatment, affecting both the forming process and the final in-service properties. This work aims to systematically investigate the influence of heat-holding time on the thermo-mechanical behaviour and post-forming properties of Al–Mg–Si alloys (EN AW 6016-T4 and EN AW 6061-T6), with a focus on optimizing process parameters to enhance formability and minimize springback. The study combines uniaxial tensile tests, cylindrical cup forming, hardness measurements, and springback evaluation, at room temperature (RT) and 200 °C, for different heat-holding times. The results show that short heat-holding times improve formability and reduce springback, while longer times promote artificial ageing, increasing strength and hardness but reducing ductility, especially in the EN AW 6016-T4 alloy. The EN AW 6061-T6 alloy exhibits greater thermal stability. The findings provide practical guidelines for industrial warm forming of Al–Mg–Si alloys, highlighting the critical role of heat-holding time in balancing formability, strength, and dimensional accuracy. Full article

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31 pages, 5554 KB

Open AccessEditor’s ChoiceArticle

Process–Design Co-Optimisation of Laser Powder Bed Fusion Titanium Gyroid Lattices via Deep Learning

by Alexander Dawes, Ali Abdelhafeez Hassan, Hany Hassanin and Khamis Essa

J. Manuf. Mater. Process. 2026, 10(3), 92; https://doi.org/10.3390/jmmp10030092 - 9 Mar 2026

Viewed by 1405

Abstract

Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and [...] Read more.

Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and yield strength were established for sheet and network gyroid architectures. To stabilise small-data learning, stacked-autoencoder pre-training was benchmarked against greedy layer-wise pre-training. Compression characterisation data at under-represented energy-density conditions were added to fill data gaps and validate predictions. The models support property-driven design in which given modulus and yield strength targets inform a method that returns feasible combinations of laser powder bed fusion settings and gyroid density and size. Pre-trained models reduced error and captured the relationship between stiffness and density and between strength and density, with yield strength prediction errors of 3.51% for sheet architectures and 8.76% for network architectures. Young’s modulus showed a higher variability that is consistent with sensitivities in LPBF such as surface roughness and thin walls. This work contributes an artificial intelligence method for manufacturing datasets using stacked autoencoder pre-training with fine-tuning, and an inverse-design workflow that maps energy density and gyroid geometry to Young’s modulus and yield strength in titanium lattices. Full article

(This article belongs to the Special Issue Digital Twinning for Manufacturing)

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14 pages, 3444 KB

Open AccessEditor’s ChoiceArticle

Scan-Strategy Dependent Microstructural Modulation in L-PBF Ti-6Al-4V Components Through Selective Rescanning

by Kalyan Nandigama, Bharath Bhushan Ravichander, Yash Parikh and Golden Kumar

J. Manuf. Mater. Process. 2026, 10(3), 88; https://doi.org/10.3390/jmmp10030088 - 2 Mar 2026

Viewed by 1318

Abstract

Laser Powder Bed Fusion (L-PBF) can enable in situ microstructural tailoring of metallic components by precisely controlling the layer-wise processing parameters. Layer rescanning is one such strategy used to induce localized microstructural modification. In this study, we investigated the effect of a lattice-based [...] Read more.

Laser Powder Bed Fusion (L-PBF) can enable in situ microstructural tailoring of metallic components by precisely controlling the layer-wise processing parameters. Layer rescanning is one such strategy used to induce localized microstructural modification. In this study, we investigated the effect of a lattice-based selective rescanning approach applied to different base scan strategies for Ti-6Al-4V samples. The lattice regions were selectively rescanned at 50% reduced laser power relative to the initial scan along the same laser path. Relative density, porosity, martensitic α′ morphology, phase fraction, and Vickers microhardness were compared with those of non-rescanned reference counterparts. Different scan strategies, including unidirectional, stripes, and chess, exhibited distinct responses to selective rescanning, resulting in localized variations in martensitic phase formation and hardness values. The extent of localized microstructural modification and hardness enhancement was strongly governed by the underlying scan strategy. Selective rescanning using the stripes strategy yielded the largest contrast between non-rescanned and rescanned regions. The unidirectional strategy showed strong effects of rescanning, but the heat-affected zones extended to the non-rescanned regions. In contrast, the chess strategy exhibited comparatively moderate changes owing to its inherent thermal-management characteristics. These findings demonstrate that selective rescanning can provide an effective, localized approach for tailoring microstructure and hardness enhancement in L-PBF Ti-6Al-4V, with its effectiveness strongly dependent on the underlying scan strategy. Full article

(This article belongs to the Special Issue Advances in Metal Additive Manufacturing: Processes, Materials, and Applications)

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17 pages, 5996 KB

Open AccessEditor’s ChoiceArticle

Optimization of the Operating Behavior of Spur Gears Through Machine Hammer Peening

by Mohammad Dadgar, Sebastian Sklenak, Martina Müller, Tim Herrig, René Greschert, Dieter Mevissen, Christian Brecher and Thomas Bergs

J. Manuf. Mater. Process. 2026, 10(3), 82; https://doi.org/10.3390/jmmp10030082 - 26 Feb 2026

Cited by 1 | Viewed by 703

Abstract

Gear systems operate under high mechanical and tribological loads, making their surfaces vulnerable to wear and fatigue. Improving surface durability requires finishing processes that improve near-surface properties and extend service life. Since machine hammer peening (MHP) offers such potential, this study investigates its [...] Read more.

Gear systems operate under high mechanical and tribological loads, making their surfaces vulnerable to wear and fatigue. Improving surface durability requires finishing processes that improve near-surface properties and extend service life. Since machine hammer peening (MHP) offers such potential, this study investigates its influence on the performance of case-hardened spur gears and evaluates its suitability as an alternative to shot peening as a conventional finishing method. Analog specimens with simplified geometries were treated using various MHP parameters to identify effective process settings. These optimized settings were then applied to real spur gears to assess performance under practical conditions. The experiments showed that MHP can significantly modify surface integrity, achieving surface roughness reductions of up to 55%, surface hardness increases of up to 30%, and compressive residual stresses exceeding −1400 MPa with stability to depths of 200 µm. These modifications resulted in improved wear and fatigue performance, with increases in load cycle number in the tooth flank up to 99% and an increase in load amplitude in the tooth root of more than 5%. For comparison, specimens were also treated with shot peening. Although MHP induced stronger surface integrity modifications, shot peening achieved higher overall load-carrying capacity because several critical areas could not be fully accessed by MHP, limiting its effectiveness. Overall, MHP shows promise as a finishing process, but its full potential depends on overcoming accessibility limitations in complex gear geometries. Full article

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34 pages, 7284 KB

Open AccessEditor’s ChoiceArticle

Wire Directed Energy Deposition Additive Manufacturing: Enabling On-Demand Medical Device Injection Mold Repurposing in Pandemic and Healthcare Supply Challenges

by Leonidas Gargalis, Evangelia K. Karaxi and Elias P. Koumoulos

J. Manuf. Mater. Process. 2026, 10(2), 63; https://doi.org/10.3390/jmmp10020063 - 12 Feb 2026

Cited by 1 | Viewed by 1686

Abstract

The COVID-19 pandemic critically emphasized the need for rapid, flexible, and decentralized manufacturing solutions to support the urgent demand for essential medical equipment, such as oximeters. Metal wire directed energy deposition—w-DED, also known as w-LMD (wire laser metal deposition)—combines the benefits of high [...] Read more.

The COVID-19 pandemic critically emphasized the need for rapid, flexible, and decentralized manufacturing solutions to support the urgent demand for essential medical equipment, such as oximeters. Metal wire directed energy deposition—w-DED, also known as w-LMD (wire laser metal deposition)—combines the benefits of high material utilization, increased printing speed, and reduced waste, making it an attractive alternative to traditional powder-based processes, especially under time-sensitive and resource-constrained conditions. This work presents a case study focusing on the design and fabrication of injection molds for oximeter casings using metal-wire-based DED. Martensitic stainless steel AISI-420 wire was employed as feedstock and processed via laser wire additive manufacturing to produce a robust, near-net-shape mold suitable for plastic injection molding. The material was selected due to good corrosion and wear resistance. However, poor ductility and toughness, together with AM-induced anisotropy, were the main challenges to address. Therefore, a multi-step methodology was defined to study the effect of different process parameters, which was validated through printing trials, and the optimum process parameter set was identified. The process enabled the rapid construction of intricate mold geometries, minimizing lead times and allowing for quick design iterations. Microstructural and physical properties such as microhardness of the as-built molds were thoroughly characterized. This case study not only illustrates the technical feasibility of producing functional injection molds via metal w-DED but also outlines its role as a resilient manufacturing pathway, capable of meeting emergent healthcare needs and supporting broader industrial applications in a post-pandemic context. Full article

(This article belongs to the Special Issue Advances in Injection Molding: Process, Materials and Applications, 2nd Edition)

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10 pages, 1613 KB

Open AccessEditor’s ChoiceArticle

Stiffness Enhancement by Means of Situational Coupling of Two Collaborative Robots

by Eckart Uhlmann, Marie-Noëlle Fielers, Thomas Pache and Onur Senyüz

J. Manuf. Mater. Process. 2026, 10(2), 54; https://doi.org/10.3390/jmmp10020054 - 4 Feb 2026

Cited by 1 | Viewed by 1187

Abstract

While collaborative robots are designed to enable flexible and safe human–robot interactions, their comparatively low structural stiffness poses a challenge for high-precision machining and heavy-assembly tasks. Addressing this limitation is essential for enhancing their performance and improving their overall efficiency in manufacturing processes. [...] Read more.

While collaborative robots are designed to enable flexible and safe human–robot interactions, their comparatively low structural stiffness poses a challenge for high-precision machining and heavy-assembly tasks. Addressing this limitation is essential for enhancing their performance and improving their overall efficiency in manufacturing processes. This paper proposes an approach for enhancing the stiffness by means of situational coupling of two collaborative robots. Therefore, an analysis is conducted to determine the kinematic limitations of coupled collaborative robots. The stiffness of coupled collaborative robots is then modeled using the finite element method. Furthermore, experimental stiffness measurements of a single collaborative robot are conducted to establish a quantitative reference, which is both to validate the model and to quantify the stiffness enhancement achieved through coupling. On the basis of the combined experimental and numerical results, it is demonstrated that the approach of coupling has the potential to enhance stiffness by up to 37.19 times in comparison with a solitary collaborative robot. Full article

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23 pages, 3101 KB

Open AccessEditor’s ChoiceArticle

Inverse Thermal Process Design for Interlayer Temperature Control in Wire-Directed Energy Deposition Using Physics-Informed Neural Networks

by Fuad Hasan, Abderrachid Hamrani, Tyler Dolmetsch, Somnath Somadder, Md Munim Rayhan, Arvind Agarwal and Dwayne McDaniel

J. Manuf. Mater. Process. 2026, 10(2), 52; https://doi.org/10.3390/jmmp10020052 - 1 Feb 2026

Cited by 1 | Viewed by 1826

Abstract

Wire-directed energy deposition (W-DED) produces steep thermal gradients and rapid heating-cooling cycles due to the moving heat source, where modest variations in process parameters significantly alter heat input per unit length and therefore the full thermal history. This sensitivity makes process tuning by [...] Read more.

Wire-directed energy deposition (W-DED) produces steep thermal gradients and rapid heating-cooling cycles due to the moving heat source, where modest variations in process parameters significantly alter heat input per unit length and therefore the full thermal history. This sensitivity makes process tuning by trial-and-error or repeated FE sweeps expensive, motivating inverse analysis. This work proposes an inverse thermal process design framework that couples single-track experiments, a calibrated finite element (FE) thermal model, and a parametric physics-informed neural network (PINN) surrogate. By using experimentally calibrated heat-loss physics to define the training constraints, the PINN learns a parameterized thermal response from physics alone (no temperature data in the PINN loss), enabling inverse design without repeated FE runs. Thermocouple measurements are used to calibrate the convection film coefficient and emissivity in the FE model, and those parameters are used to train a parametric PINN over continuous ranges of arc power (1.5–3.0 kW) and travel speed (0.005–0.015 m/s) without using temperature data in the loss function. The trained PINN model was validated against the calibrated FE model at 3 probe locations with different power and travel speed combinations. Across these benchmark conditions, the mean absolute errors are between 6.5–17.4 °C, with cooling-tail errors ranging from 1.8–12.1 °C. The trained surrogate is then embedded in a sampling-based inverse optimization loop to identify power-speed combinations that achieve prescribed interlayer temperatures at a fixed dwell time. For target interlayer temperatures of 100, 130, and 160 °C with a 10 s dwell time, the optimized solutions remain within 3.3–5.6 °C of the target according to the PINN, while FE verification is within 4.0–6.6 °C. The results demonstrate that a physics-only parametric PINN surrogate enables inverse thermal process design without repeated FE runs while establishing a single-track baseline for extension to multi-track and multi-layer builds. Full article

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31 pages, 5647 KB

Open AccessEditor’s ChoiceArticle

Experimental Study on Sandwich Composites with Carbon Fiber Fabric Skin and 3D-Printed Carbon Fiber-Reinforced Polyamide Core

by Sotiria Dimitrellou, Isidoros Iakovidis, Gerasimos Stratos and Dimitrios-Nikolaos Pagonis

J. Manuf. Mater. Process. 2025, 9(11), 361; https://doi.org/10.3390/jmmp9110361 - 4 Nov 2025

Cited by 2 | Viewed by 3096

Abstract

Material extrusion is a widely employed additive manufacturing technique with the functional capability of fabricating solid objects or cellular structures by depositing molten thermoplastic material in successive layers according to the designed path of deposition beads. Carbon fiber sandwich composites are advanced structures, [...] Read more.

Material extrusion is a widely employed additive manufacturing technique with the functional capability of fabricating solid objects or cellular structures by depositing molten thermoplastic material in successive layers according to the designed path of deposition beads. Carbon fiber sandwich composites are advanced structures, ideal in applications that require high strength and stiffness, and low weight. In the present work, sandwich composites consisting of a carbon fiber-reinforced polyamide core—3D printed with a cubic pattern at 50% infill density—and carbon fiber fabric (CFF) skin, were fabricated using the hand lay-up method and experimentally investigated. The results showed that the tensile, flexural and impact strength of the sandwich composites increased by 64.5%, 24.5% and 69.0%, respectively, compared to unreinforced 3D printed specimens with 50% infill density, and by 24.3%, 18.8% and 56.3%, respectively, compared to unreinforced 3D printed specimens with 100% infill density. In addition, a reduction in the water absorption and the density of the sandwich composites was observed. Similar results were obtained for sandwich composites with one additional internal CFF layer. This work demonstrates that this specific combination of materials and manufacturing processes can be successfully employed for lightweight, water-resistant carbon fiber sandwich structures with improved mechanical strength. Full article

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19 pages, 7538 KB

Open AccessEditor’s ChoiceArticle

Study on the Layered Structure of Ceramic-Side Bonding Area and the Mechanical Property of Al₂O₃–Kovar Brazed Joint with Ag-Cu-Ti Filler

by Junjie Qi, Dong Du, Dongqi Zhang, Shuai Xue, Jiaming Zhang, Jiamin Yi, Haifei You and Baohua Chang

J. Manuf. Mater. Process. 2025, 9(11), 355; https://doi.org/10.3390/jmmp9110355 - 29 Oct 2025

Cited by 1 | Viewed by 1572

Abstract

During active brazing of alumina ceramics, active elements react with the ceramic to form a reaction layer, which has significant influence on the mechanical property of the brazed joint. However, the composition and formation mechanism of this layer remain unclear among researchers. To [...] Read more.

During active brazing of alumina ceramics, active elements react with the ceramic to form a reaction layer, which has significant influence on the mechanical property of the brazed joint. However, the composition and formation mechanism of this layer remain unclear among researchers. To fill this gap, different brazing temperatures (900–1100 °C) and heating rates (2.5 °C/min and 10 °C/min) were used to braze 95% Al₂O₃ ceramics and a Kovar 4J34 alloy using a Ag-Cu-2Ti active brazing filler, and the microstructure and mechanical properties of the joints were investigated. The results show that the joint could be divided into five layers: Al₂O₃, ceramic-side reaction layer, filler layer, Kovar-side reaction layer, and Kovar. The ceramic-side reaction layer could be further divided into a Ti-O-rich layer and an intermetallics (IMC)-rich layer, and the Kovar-side reaction layer consists of TiFe₂ particles, Ag-Cu eutectic, and the remaining Kovar. A belt-like TiFe₂+TiNi₃ IMC could be found in the filler layer. Increasing the brazing temperature enlarged the belt-like TiFe₂+TiNi₃ IMC in the filler layer and increased the thickness of the IMC-rich layer in the ceramic-side reaction layer, but had no significant effect on the thickness of the Ti-O-rich layer in the ceramic-side reaction layer. A lower heating rate (2.5 °C/min) was found to suppress the formation of the IMC-rich layer and shift the fracture location in shear tests from the ceramic-side reaction layer to the filler layer, indicating that the strength of the ceramic-side reaction layer was enhanced by controlling the formation of the IMC-rich layer. A maximum shear strength of 170 ± 61 MPa was obtained at a heating rate of 2.5 °C/min and a brazing temperature of 940 °C. Full article

(This article belongs to the Special Issue Advances in Welding Technology: 2nd Edition)

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22 pages, 5120 KB

Open AccessEditor’s ChoiceArticle

Auxetic Systems Fabricated Using Mycelia-Based Composites

by Claudia Cadinu, Ruben Gatt, Renald Blundell, Joseph N. Grima and Pierre-Sandre Farrugia

J. Manuf. Mater. Process. 2025, 9(11), 353; https://doi.org/10.3390/jmmp9110353 - 28 Oct 2025

Cited by 1 | Viewed by 1473

Abstract

In an era of increasingly problematic global waste management, innovative materials can hold the key to a circular economy. Within such a context, this work aimed at manufacturing, potentially for the first time, auxetic material (i.e., materials with a negative Poisson’s ratio) using [...] Read more.

In an era of increasingly problematic global waste management, innovative materials can hold the key to a circular economy. Within such a context, this work aimed at manufacturing, potentially for the first time, auxetic material (i.e., materials with a negative Poisson’s ratio) using mycelia-based composites (i.e., materials made by growing fungi on a biodegradable substrate). The fabrication of the auxetic biomaterials was undertaken using both direct growth methods in purposely designed molds and through subtractive manufacturing of commercially available mycelia panels. In the former case, various substrates were employed, differing in sawdust granulation and nutrient content. Furthermore, enhancements in the form of scaffolds and hay were considered with the aim of improving the cohesion and elastic properties of the end product. Mechanical testing of the samples produced showed that both manufacturing methods can produce structures capable of exhibiting a negative Poisson’s ratio. At the same time, the intrinsic brittle nature of mycelia-based composites limits the compactness and cohesion of the end products. In this context, the different methodologies employed to improve these properties yielded some promising results. Thus, while this work showed that indeed auxetic biomaterials can be fabricated, the manufacturing methods still require further improvement to produce better-performing specimens. Full article

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18 pages, 3059 KB

Open AccessEditor’s ChoiceArticle

Influence of Substrate Type Made of WC-Co on CrN/CrAlN Coatings’ Durability During Machining of Particleboard

by Paweł Czarniak, Beata Kucharska, Karol Szymanowski, Corinne Nouveau, Denis Lagadrillere, Marek Betiuk, Tomasz Rygier, Krzysztof Kulikowski, Zbigniew Kusznierewicz and Jerzy Robert Sobiecki

J. Manuf. Mater. Process. 2025, 9(11), 349; https://doi.org/10.3390/jmmp9110349 - 24 Oct 2025

Viewed by 1124

Abstract

This paper investigates the influence of substrate grain size on the behavior of a multilayer CrN/CrAlN coating, with the bilayer thickness varying across the cross-section in the range of 200–1000 nm. The substrate tools were made of WC-Co sintered carbide with three different [...] Read more.

This paper investigates the influence of substrate grain size on the behavior of a multilayer CrN/CrAlN coating, with the bilayer thickness varying across the cross-section in the range of 200–1000 nm. The substrate tools were made of WC-Co sintered carbide with three different grain sizes. The coatings were subjected to mechanical and tribological tests to assess their performance, including nanohardness, scratch resistance, and tribological testing. The coating’s roughness was measured using a 2D profilometer. Additionally, the chemical composition and surface morphology were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). The durability tests were performed on an industrial CNC machine tool on the particleboard. The results revealed that tools with ultra-fine nano-grain (S) and micro-grain (T) WC-Co substrates exhibited a significant increase in tool durability by 28% and 44%, respectively. Significant differences in the microgeometry of the substrate U, especially in relation to the tool based on substrate S, explain the lack of improvement in its durability despite the use of a multilayer coating. Full article

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13 pages, 5364 KB

Open AccessEditor’s ChoiceArticle

Improved Machinability of Pockets in a Liquid-Silicon-Infiltrated Silicon Carbide Composite Using Ultrasonic Assistance

by Achim Rösiger, Patricia León-Pérez, Joshua Macken and Ralf Goller

J. Manuf. Mater. Process. 2025, 9(11), 346; https://doi.org/10.3390/jmmp9110346 - 22 Oct 2025

Viewed by 1061

Abstract

Surface finishing processes are required to produce the final shape of components made of the silicon-infiltrated silicon carbide composite Cesic^® from ECM (Engineered Ceramic Materials GmbH, 85452 Moosinning, Germany). Electrical discharge machining (EDM) is still the most effective method for manufacturing pockets [...] Read more.

Surface finishing processes are required to produce the final shape of components made of the silicon-infiltrated silicon carbide composite Cesic^® from ECM (Engineered Ceramic Materials GmbH, 85452 Moosinning, Germany). Electrical discharge machining (EDM) is still the most effective method for manufacturing pockets and mounts in 3D-shaped ceramic satellite components for space applications. NC-grinding is not used, because it results in high grinding loads and rapid tool wear when applied to Cesic^®. In contrast to planar machining, tool wear during NC-grinding with small tools is particularly critical, as it alters the tool geometry and consequently causes deviations in the workpiece geometry. Ultrasonic-assisted grinding offers a promising alternative to overcome the low material removal rates and long processing times associated with EDM while simultaneously enhancing tool life, thus enabling more economical and reliable production. In this experimental study, both conventional grinding (CG) and ultrasonic-assisted grinding (UAG) processes are compared and used to machine Cesic^®. In order to verify the effect of the ultrasonic vibration, analyses of amplitude and frequency are performed. During machining experiments, the grinding loads are measured. The influence of different machining conditions on surface quality is evaluated concerning the roughness of the machined specimens. Compared to CG, UAG shows lower tool wear, owing to the self-cleaning effects caused by the ultrasonic oscillation of the tool. Consequently, the stability of the NC-grinding process is significantly improved. Full article

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21 pages, 9525 KB

Open AccessEditor’s ChoiceArticle

Experimental and Finite Element Analysis of Refill Friction Stir Spot Welding in Dissimilar 6061-T6 and 5052-H321 Aluminum Alloys

by Dan Cătălin Bîrsan and Vasile Bașliu

J. Manuf. Mater. Process. 2025, 9(10), 341; https://doi.org/10.3390/jmmp9100341 - 19 Oct 2025

Cited by 5 | Viewed by 1408

Abstract

This study presents an integrated experimental and numerical investigation of the Refill Friction Stir Spot Welding (RFSSW) process applied to dissimilar aluminum alloys. The primary objective is to evaluate the mechanical and thermal behavior of the joints and to identify key process parameters [...] Read more.

This study presents an integrated experimental and numerical investigation of the Refill Friction Stir Spot Welding (RFSSW) process applied to dissimilar aluminum alloys. The primary objective is to evaluate the mechanical and thermal behavior of the joints and to identify key process parameters influencing weld quality. Experimental welding trials were performed on aluminum alloy sheets using RFSSW, followed by shear testing and metallographic analysis to assess joint integrity, microstructure evolution, and fracture behavior. Infrared thermography and temperature sensors were employed to monitor heat distribution during welding. In parallel, a finite element model was developed to simulate the thermal cycle and stress distribution within the welded region. The numerical results showed good agreement with the experimental data, particularly regarding peak temperature and cooling trends at specific distances from the tool center. The findings demonstrate that RFSSW can successfully join dissimilar aluminum alloys with minimal defects when optimized parameters are applied. The combination of experimental observations and FEM simulation provides valuable insights into the underlying thermomechanical phenomena and offers a foundation for further process optimization. Full article

(This article belongs to the Special Issue Recent Developments in Friction Stir Welding Technology and Applications)

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43 pages, 2436 KB

Open AccessEditor’s ChoiceReview

Fabricating Three-Dimensional Metamaterials Using Additive Manufacturing: An Overview

by Balakrishnan Subeshan, Abdulhammed K. Hamzat and Eylem Asmatulu

J. Manuf. Mater. Process. 2025, 9(10), 343; https://doi.org/10.3390/jmmp9100343 - 19 Oct 2025

Cited by 5 | Viewed by 6285

Abstract

Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not [...] Read more.

Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not up to the task of creating metamaterials, which are now more accurately and more effectively analyzed than they were in the past. Recent advances in additive manufacturing (AM) have achieved remarkable success, with ensemble machine learning models demonstrating R² values exceeding 0.97 and accuracy improvements of 9.6% over individual approaches. State-of-the-art multiphoton polymerization (MPP) techniques now reach submicron resolution (<1 μm), while selective laser melting (SLM) processes provide 20–100 μm precision for metallic metamaterials. This work offers a comprehensive review of additively manufactured 3D metamaterials, focusing on three categories of their fabrication: electromagnetic (achieving bandgaps up to 470 GHz), acoustic (providing 90% sound suppression at targeted frequencies), and mechanical (demonstrating Poisson’s ratios from −0.8 to +0.8). The relationship between different types of AM processes used in creating 3D objects and the properties of the resulting materials has been systematically reviewed. This research aims to address gaps and develop new applications to meet the modern demand for the broader use of metamaterials in advanced devices and systems that require high efficiency for sophisticated, high-performance applications. Full article

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18 pages, 3666 KB

Open AccessEditor’s ChoiceArticle

Reinforcement Learning Enabled Intelligent Process Monitoring and Control of Wire Arc Additive Manufacturing

by Allen Love, Saeed Behseresht and Young Ho Park

J. Manuf. Mater. Process. 2025, 9(10), 340; https://doi.org/10.3390/jmmp9100340 - 18 Oct 2025

Cited by 11 | Viewed by 3659

Abstract

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such [...] Read more.

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

(This article belongs to the Special Issue Advancing Wire Arc Additive Manufacturing (WAAM) for Metallic Component Manufacture: Recent Developments and Challenges)

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32 pages, 3888 KB

Open AccessEditor’s ChoiceReview

AI-Driven Innovations in 3D Printing: Optimization, Automation, and Intelligent Control

by Fatih Altun, Abdulcelil Bayar, Abdulhammed K. Hamzat, Ramazan Asmatulu, Zaara Ali and Eylem Asmatulu

J. Manuf. Mater. Process. 2025, 9(10), 329; https://doi.org/10.3390/jmmp9100329 - 7 Oct 2025

Cited by 43 | Viewed by 16772

Abstract

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization [...] Read more.

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI. Full article

(This article belongs to the Special Issue Advances in 3D Printing Technologies: Materials, Processes, and Applications)

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23 pages, 6532 KB

Open AccessEditor’s ChoiceArticle

Interfacial Strength Testing of Laser Powder Bed Fusion Metal Samples Produced Using the Multi-Material Binning Method

by Suyash Niraula, Brendon S. Dodge, Justin D. Gillham and Thomas A. Berfield

J. Manuf. Mater. Process. 2025, 9(10), 327; https://doi.org/10.3390/jmmp9100327 - 3 Oct 2025

Cited by 1 | Viewed by 2460

Abstract

Creating complex structures using multiple materials in additive manufacturing comes with a unique set of challenges, particularly when it comes to how the materials transition and bond together. This research looks at a new powder binning method for combining metal powders to create [...] Read more.

Creating complex structures using multiple materials in additive manufacturing comes with a unique set of challenges, particularly when it comes to how the materials transition and bond together. This research looks at a new powder binning method for combining metal powders to create multi-material components in a single build, all produced on a standard Laser Powder Bed Fusion EOS M 290 machine. The study focuses on the size and quality of the resulting multi-material interfaces and how different scan strategies used affect the interface strength. The strength of the interface between different material pairings is evaluated for combinations of 316 stainless steel bonded to Inconel 718, Inconel 718 bonded to Inconel 625, and Inconel 625 bonded to 316 stainless steel. The Ultimate Tensile Strength (UTS) and interface region lengths were calculated to be 675 MPa and 1250 µm for 316L–IN718, 1004 MPa and 2500 µm for IN718–IN625, and 687 MPa and 2000 µm for IN625–316L, respectively. The findings show that the laser powder bed fusion material binning method is comparable to traditional methods, such as welding or directed energy deposition. This suggests that the new material binning method offers clear advantages when it comes to enabling complex geometry multi-material components while maintaining the strength and durability of the bonds between different metal materials found in traditional means. Further, optimization of scan strategies in the interface zones could play a significant role in improving the overall performance of these multi-material components, which is particularly important for industries such as aerospace, automotive, and energy production. Full article

(This article belongs to the Special Issue Design, Processes and Materials for Additive Manufacturing: 2nd Edition)

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25 pages, 6338 KB

Open AccessEditor’s ChoiceArticle

Multi-Scale Model of Mid-Frequency Errors in Semi-Rigid Tool Polishing of Diamond-Turned Electroless Nickel Mirror

by Pengfeng Sheng, Jingjing Xia, Jun Yu, Kun Wang and Zhanshan Wang

J. Manuf. Mater. Process. 2025, 9(10), 325; https://doi.org/10.3390/jmmp9100325 - 30 Sep 2025

Viewed by 1315

Abstract

Semi-rigid tool polishing is widely used in the high-precision manufacturing of electroless nickel surface due to its stable material removal and high efficiency in correcting mid- and high-frequency profile errors. However, predicting mid-frequency errors remains challenging due to the complexity of their underlying [...] Read more.

Semi-rigid tool polishing is widely used in the high-precision manufacturing of electroless nickel surface due to its stable material removal and high efficiency in correcting mid- and high-frequency profile errors. However, predicting mid-frequency errors remains challenging due to the complexity of their underlying sources. In this study, a theoretical model for semi-rigid tool polishing was developed based on multi-scale contact theory, incorporating a bridging model, rough surface contact, and Hertzian contact mechanics. The model accounts for the effects of tool surface roughness, polishing force, and path spacing. A series of experiments on diamond-turned electroless nickel mirrors was conducted to quantitatively evaluate the model’s feasibility and accuracy. The results demonstrate that the model can effectively predict mid-frequency errors, reveal the material removal mechanisms in semi-rigid polishing, and guide the optimization of process parameters. Ultimately, a surface with mid-frequency errors of 0.59 nm Rms (measured over a 1.26 mm × 0.94 mm window) was achieved, closely matching the predicted value of 0.64 nm. Full article

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21 pages, 10551 KB

Open AccessEditor’s ChoiceArticle

Bayesian Model Updating for Chatter in Milling

by Ali Ebrahimi-Tirtashi and Keivan Ahmadi

J. Manuf. Mater. Process. 2025, 9(10), 323; https://doi.org/10.3390/jmmp9100323 - 26 Sep 2025

Viewed by 1644

Abstract

The modal parameters of tooltip vibrations are crucial for determining chatter-free machining conditions. However, conventional methods often depend on measurements taken when the machine is not operating under real cutting conditions or require multiple experiments under chatter conditions, which is time-consuming and impractical [...] Read more.

The modal parameters of tooltip vibrations are crucial for determining chatter-free machining conditions. However, conventional methods often depend on measurements taken when the machine is not operating under real cutting conditions or require multiple experiments under chatter conditions, which is time-consuming and impractical for real-world manufacturing. This paper proposes a Bayesian Model Updating (BMU) approach to improve the chatter model parameters using experimental observations collected during normal, stable milling operations. Operational Modal Analysis (OMA) is adopted to extract the system dynamics from the in-process signals. These results are subsequently integrated into the BMU framework, updating the initial model parameters to reflect actual cutting conditions. The effectiveness of this approach is demonstrated through an experimental case study, highlighting its feasibility and potential for industrial applications. Full article

(This article belongs to the Special Issue New Trends in Precision Machining Processes)

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23 pages, 8159 KB

Open AccessEditor’s ChoiceArticle

Assessment of Pinless Tools Efficiency in FSSW of Thick High-Strength Low-Alloy Steels for Structural Lap Joints

by David Gomes Andrade, Sree Sabari, Carlos Leitão and Dulce M. Rodrigues

J. Manuf. Mater. Process. 2025, 9(9), 317; https://doi.org/10.3390/jmmp9090317 - 17 Sep 2025

Viewed by 1352

Abstract

In this study, the maximum achievable weldable thickness in spot welding with pinless tools was investigated by welding multiple stacks of 1 mm thick high-strength steel HC420LA. This approach allowed for the evaluation of heat dissipation during the welding of progressively thicker sections, [...] Read more.

In this study, the maximum achievable weldable thickness in spot welding with pinless tools was investigated by welding multiple stacks of 1 mm thick high-strength steel HC420LA. This approach allowed for the evaluation of heat dissipation during the welding of progressively thicker sections, as well as for the comparison of the joints strength for increasing welded thicknesses. The methodology used also enabled the identification of the key technical challenges in achieving high-quality welds using very short welding times. Notably, even for a 5 s weld, a four-sheet configuration (4 mm thickness) was successfully joined, achieving a maximum shear load of approximately 10 kN and exhibiting a refined acicular ferritic bainitic microstructure with metallic continuity across the steel interfaces. The highest mechanical performance was obtained in the welds produced within 30 s, which reached a maximum shear load of approximately 16 kN. Importantly, the mechanical strength of these welds was benchmarked against relevant standards for the automotive and construction sectors, demonstrating that the spot welding with pinless tools enable achieving performance levels suitable for structural applications. Full article

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20 pages, 18687 KB

Open AccessEditor’s ChoiceArticle

Influence of Stirring Pin Geometry on Weld Appearance and Microstructure in Wire-Based Friction-Stir Additive Manufacturing of EN AW-6063 Aluminium

by Stefan Donaubauer, Stefan Weihe and Martin Werz

J. Manuf. Mater. Process. 2025, 9(9), 306; https://doi.org/10.3390/jmmp9090306 - 5 Sep 2025

Cited by 2 | Viewed by 2764

Abstract

Additive manufacturing of metal components is predominantly based on fusion-welding processes involving melting and solidification. However, processing high-strength aluminium alloys presents challenges, including reduced mechanical properties and increased susceptibility to hot cracking. To address these issues, alternative solid-state processing methods for aluminium are [...] Read more.

Additive manufacturing of metal components is predominantly based on fusion-welding processes involving melting and solidification. However, processing high-strength aluminium alloys presents challenges, including reduced mechanical properties and increased susceptibility to hot cracking. To address these issues, alternative solid-state processing methods for aluminium are being explored worldwide. One such method is wire-based friction-stir additive manufacturing, which builds on the principles of friction-stir welding. This study focused on assessing a range of pin tool designs to promote improved mixing between the filler material and substrate. The best results were achieved using a two-stirring-probe configuration, which was then employed to fabricate a multilayer wall made of EN AW-6063 aluminium alloy. The resulting structure showed significant grain refinement, with the deposited layers having an average grain size approximately four times smaller than that of the substrate, indicating dynamic recrystallisation. Tensile testing of the intermediate layer revealed a strength of 147 MPa and 10% elongation, corresponding to 77% of the filler wire strength. These findings highlight the potential of the W-FSAM process for producing near-net-shape, high-quality lightweight metal components with refined microstructures and reliable mechanical performance. Full article

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24 pages, 11780 KB

Open AccessEditor’s ChoiceArticle

Additive Manufacturing of Carbon Fiber Cores for Sandwich Structures: Optimization of Infill Patterns and Fiber Orientation for Improved Impact Resistance

by Claudio Tosto, Lorena Saitta, Ignazio Blanco, Gabriele Fichera, Mattia Evangelista, Jerin Jose, Alessia Pantaleoni and Irene Bavasso

J. Manuf. Mater. Process. 2025, 9(9), 299; https://doi.org/10.3390/jmmp9090299 - 1 Sep 2025

Cited by 6 | Viewed by 3329

Abstract

Carbon fiber-reinforced composites (CFRCs) are widely used in aerospace, automotive, and defense applications due to their high strength-to-weight ratio and excellent mechanical performance. In this study, cores and sandwich panels were fabricated via fused filament fabrication (FFF) using co-polyester filaments reinforced with 20 [...] Read more.

Carbon fiber-reinforced composites (CFRCs) are widely used in aerospace, automotive, and defense applications due to their high strength-to-weight ratio and excellent mechanical performance. In this study, cores and sandwich panels were fabricated via fused filament fabrication (FFF) using co-polyester filaments reinforced with 20 wt.% short carbon fibers. The mechanical response of the structures was evaluated under low-velocity impact (LVI) conditions using instrumented drop weight testing at energy levels ranging from 2 to 20 J. A three-factor, three-level full factorial experimental design was employed, considering build orientation (flat vs. upright), infill pattern (trihexagonal vs. triangular), and impact energy as factors. The maximum contact force was selected as the primary response variable. The results revealed that upright-printed specimens exhibited significantly improved impact resistance compared to flat-printed ones, with increases in peak force of up to 28% for cores and over 68% for sandwich structures. Among the tested infill geometries, the triangular pattern outperformed the trihexagonal one across all configurations and energy levels. The combination of upright orientation and triangular infill proved to be the most effective, providing enhanced energy absorption and reduced rear-side damage, especially under higher impact energies. These findings offer valuable insights into the design of lightweight, impact-resistant structures produced by additive manufacturing, with direct implications for structural components in demanding engineering environments. Full article

(This article belongs to the Special Issue Innovative and Sustainable Advances in Polymer Composites for Additive Manufacturing: Processing, Microstructure, Machining, and Mechanical Properties)

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38 pages, 4536 KB

Open AccessEditor’s ChoiceReview

Emerging Technologies in Augmented Reality (AR) and Virtual Reality (VR) for Manufacturing Applications: A Comprehensive Review

by Nitol Saha, Victor Gadow and Ramy Harik

J. Manuf. Mater. Process. 2025, 9(9), 297; https://doi.org/10.3390/jmmp9090297 - 1 Sep 2025

Cited by 11 | Viewed by 17028

Abstract

As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological [...] Read more.

As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological advancements and applications of AR and VR within the context of manufacturing. This review also encompasses the utilization of AR and VR technologies across different stages of the manufacturing process, including design, prototyping, assembly, training, maintenance, and quality control. Furthermore, this review highlights the recent developments in hardware and software components that have facilitated the adoption of AR and VR in manufacturing environments. This comprehensive literature review identifies the emerging technologies that are driving AR and VR technology toward technological maturity for implementation in manufacturing applications. Finally, this review discusses the major difficulties in implementing AR and VR technologies in the manufacturing sectors. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)

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22 pages, 4270 KB

Open AccessEditor’s ChoiceArticle

Thermal Characterization and Predictive Modeling of Thermo-Elastic Errors in Five-Axis Machining Centers Using Dynamic R-Test

by Tae Hun Lee, Tim Klinkhammer, Daniel Zontar and Christian Brecher

J. Manuf. Mater. Process. 2025, 9(9), 293; https://doi.org/10.3390/jmmp9090293 - 31 Aug 2025

Viewed by 4616

Abstract

Five-axis machining centers are essential for manufacturing complex, high-precision parts. However, their accuracy is significantly affected by thermally induced geometric errors, also known as thermo-elastic errors. This paper presents a comprehensive approach to thermal characterization and its potential application in predictive modeling on [...] Read more.

Five-axis machining centers are essential for manufacturing complex, high-precision parts. However, their accuracy is significantly affected by thermally induced geometric errors, also known as thermo-elastic errors. This paper presents a comprehensive approach to thermal characterization and its potential application in predictive modeling on a five-axis machine tool demonstrator, showcasing the capabilities of a novel dynamic R-test measurement method. Based on a previously developed and validated dynamic R-test measurement method that enables the rapid, volumetric acquisition of machine deviations during continuous movement, detailed experimental investigations were conducted under various single- and combined-axis loading scenarios. The extensive dataset and detailed error information provided by the dynamic R-test method enabled thorough analysis and correlation of thermo-elastic errors, including translational and rotational errors, with temperature and control-internal axis data. A well-established phenomenological model based on PT1 transfer functions is used, detailing its input variables and parameter determination methods. The model’s predictive capability was rigorously validated against independent datasets, demonstrating significant reductions in primary errors (up to 70% in maximum residual and 80% in RMSE). This study identifies the most influential error types and their correlation with thermal loads. This confirms the feasibility of robustly predicting thermo-elastic behavior and enhancing the volumetric accuracy of five-axis machine tools, particularly by leveraging the detailed error insights enabled by the dynamic R-test. Full article

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56 pages, 25615 KB

Open AccessEditor’s ChoiceReview

Recent Progress and Scientific Challenges in Wire-Arc Additive Manufacturing of Metallic Multi-Material Structures

by Sainand Jadhav, Sambhaji Kusekar, Akash Belure, Satyavan Digole, Abhijeet Mali, Muralimohan Cheepu, Manoj Mugale, Suhas Alkunte and Duckbong Kim

J. Manuf. Mater. Process. 2025, 9(8), 284; https://doi.org/10.3390/jmmp9080284 - 21 Aug 2025

Cited by 24 | Viewed by 9355

Abstract

Metallic multi-material structures are heterogeneous structures characterized by changing composition, microstructures, and site-specific characteristics, advantageous for numerous applications where multifunctionality is desired. Metallic multi-material structures are known as bimetallic structures (BSs), which are functionally graded materials (FGMs). In recent years, wire-arc additive manufacturing [...] Read more.

Metallic multi-material structures are heterogeneous structures characterized by changing composition, microstructures, and site-specific characteristics, advantageous for numerous applications where multifunctionality is desired. Metallic multi-material structures are known as bimetallic structures (BSs), which are functionally graded materials (FGMs). In recent years, wire-arc additive manufacturing (WAAM) advanced as a promising additive manufacturing process to realize the fabrication of these structures due to its high deposition rate, cost-effectiveness, and material utilization efficiency. This review presents a comprehensive overview of the recent progress, processing strategies, and scientific challenges in WAAM of multi-material structures. The paper begins with an introduction to multi-material structures, followed by a bibliometric analysis of the current research landscape. Conventional and additive manufacturing fabrication approaches are presented. The review highlights key developments in processing strategies and critically evaluates research studies on WAAM of BS and FGMs. Major scientific challenges, including porosity, lack of fusion, residual stresses, cracking, material compatibility, and brittle intermetallic phase formation, are critically analyzed. Additionally, modeling, simulation, and process automation issues are discussed as barriers to industrial-scale implementation. The paper concludes with an outlook on future research directions to address existing challenges and accelerate the adoption of WAAM for complex multi-material components. Full article

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20 pages, 7113 KB

Open AccessEditor’s ChoiceArticle

Extrusion 3D-Printed Kaolinite Ceramic Filters for Water Applications

by Rawan Elsersawy, Romina Donyadari and Mohammad Abu Hasan Khondoker

J. Manuf. Mater. Process. 2025, 9(8), 278; https://doi.org/10.3390/jmmp9080278 - 14 Aug 2025

Cited by 3 | Viewed by 3351

Abstract

Ceramic materials have been utilized for centuries across a range of industries due to their chemical stability and porous microstructure. One prominent application is water filtration, where ceramics offer an effective medium for removing contaminants. Ceramic filters can operate under either pressure-driven or [...] Read more.

Ceramic materials have been utilized for centuries across a range of industries due to their chemical stability and porous microstructure. One prominent application is water filtration, where ceramics offer an effective medium for removing contaminants. Ceramic filters can operate under either pressure-driven or gravity-driven mechanisms. While traditional fabrication techniques, such as pottery, have been historically employed to produce ceramic filters, these methods are limited by user skills, lack of reproducibility, and geometric constraints. In contrast, modern additive manufacturing techniques provide enhanced precision, repeatability, and customization. This study employs extrusion-based 3D printing to fabricate gravity-driven ceramic filters with tailored geometries to meet specific performance requirements. The use of 3D printing allows for efficient production of uniform filters with optimized internal structures. The selected ceramic material, derived from natural sources, offers environmental compatibility, as it is both sustainable and biodegradable. The fabricated filters were evaluated for their effectiveness in treating water. The filtration tests showed significant improvements in water quality, including reduced turbidity, color, iron, manganese, and total and calcium hardness. pH increased from 6.23 to 7.26, and conductivity dropped from 7.43 mS to 4.5 mS, indicating effective ion removal. These findings highlight the potential of 3D-printed ceramic filters as an environmentally friendly and effective solution for decentralized water purification applications. Full article

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20 pages, 4555 KB

Open AccessEditor’s ChoiceArticle

An Experimental Study on Ultrasonic-Assisted Drilling of CFRP Composites with Minimum Quantity Lubrication

by Ramazan Hakkı Namlu, Mustafa Burak Sağener, Zekai Murat Kılıç, Oguz Colak and Sadık Engin Kılıç

J. Manuf. Mater. Process. 2025, 9(8), 276; https://doi.org/10.3390/jmmp9080276 - 12 Aug 2025

Cited by 7 | Viewed by 3245

Abstract

The increasing use of carbon fiber reinforced polymer (CFRP) composites in industries such as aerospace, due to its high strength-to-weight ratio, durability, and resistance to corrosion has led to a growing demand for more efficient machining processes. However, the multilayered structure of CFRP [...] Read more.

The increasing use of carbon fiber reinforced polymer (CFRP) composites in industries such as aerospace, due to its high strength-to-weight ratio, durability, and resistance to corrosion has led to a growing demand for more efficient machining processes. However, the multilayered structure of CFRP composites, composed of densely packed fibers, presents significant challenges during machining. Additionally, when cutting fluids are used to improve effective cooling and lubrication, the material tends to absorb the fluid, causing damage and leading to problem of weaking of composite structure. To address these issues, this study compares ultrasonic-assisted drilling (UAD) and minimum quantity lubrication (MQL) techniques with conventional drilling (CD) and dry cutting to improve the performance of CFRP composite drilling. The results show that using UAD and MQL together reduced thrust force by up to 27%, improved surface roughness inside the holes by up to 31%, reduced improved hole diameter, cylindricity, roundness, and delamination. Full article

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21 pages, 4954 KB

Open AccessEditor’s ChoiceArticle

Direct Ink Writing and Characterization of ZrC-Based Ceramic Pellets for Potential Nuclear Applications

by Narges Malmir, Guang Yang, Thomas Poirier, Nathaniel Cavanaugh, Dong Zhao, Brian Taylor, Nikhil Churi, Tiankai Yao, Jie Lian, James H. Edgar, Dong Lin and Shuting Lei

J. Manuf. Mater. Process. 2025, 9(8), 270; https://doi.org/10.3390/jmmp9080270 - 11 Aug 2025

Viewed by 2509

Abstract

Developing advanced nuclear fuel technologies is critical for high-performance applications such as nuclear thermal propulsion (NTP). This study explores the feasibility of direct ink writing (DIW) for fabricating ceramic pellets for potential nuclear applications. Zirconium carbide (ZrC) is used as a matrix material [...] Read more.

Developing advanced nuclear fuel technologies is critical for high-performance applications such as nuclear thermal propulsion (NTP). This study explores the feasibility of direct ink writing (DIW) for fabricating ceramic pellets for potential nuclear applications. Zirconium carbide (ZrC) is used as a matrix material and vanadium carbide (VC) is used as a surrogate for uranium carbide (UC) in this study. A series of ink formulations were developed with varying concentrations of VC and nanocrystalline cellulose (NCC) to optimize the rheological properties for DIW processing. Post-sintering analysis revealed that conventionally sintered samples at 1750 °C exhibited high porosity (>60%), significantly reducing the compressive strength compared to dense ZrC ceramics. However, increasing VC content improved densification and mechanical properties, albeit at the cost of increased shrinkage and ink flow challenges. Spark plasma sintering (SPS) achieved near-theoretical density (~97%) but introduced geometric distortions and microcracking. Despite these challenges, this study demonstrates that DIW offers a viable route for fabricating ZrC-based ceramic structures, provided that sintering strategies and ink rheology are further optimized. These findings establish a baseline for DIW of ZrC-based materials and offer valuable insights into the porosity control, mechanical stability, and processing limitations of DIW for future nuclear fuel applications. Full article

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52 pages, 5052 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Sustainable and Green Additive Manufacturing: Technologies, Practices, and Future Directions

by Sudip Dey Dipta, Md. Mahbubur Rahman, Md. Jonaet Ansari and Md. Nizam Uddin

J. Manuf. Mater. Process. 2025, 9(8), 269; https://doi.org/10.3390/jmmp9080269 - 9 Aug 2025

Cited by 20 | Viewed by 8375

Abstract

Additive manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology across various industries due to its potential for design flexibility, material efficiency, and reduced production lead times. As global attention increasingly shifts toward environmental sustainability, there is a growing [...] Read more.

Additive manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology across various industries due to its potential for design flexibility, material efficiency, and reduced production lead times. As global attention increasingly shifts toward environmental sustainability, there is a growing need to evaluate the ecological implications and opportunities associated with AM. This comprehensive review explores the current state of sustainable and green additive manufacturing (SGAM) technologies and practices, highlighting innovations that reduce energy consumption, minimize material waste, and incorporate renewable or recyclable materials. This study focuses on the utilization of recyclable thermoplastics combined with biodegradable polymers, exploring sustainable source materials, cold fabrication techniques, and cyclic lifecycle strategies integrated with renewable energy systems. Despite its potential, SGAM faces key challenges such as material compatibility, scalability of manufacturing processes, mechanical property optimization, and the need for standardized production protocols. Nevertheless, this work finds that SGAM devices are effective in minimizing environmental impact across the entire manufacturing process, aligning with predominant research trends that emphasize strategic predictive models to guide future developments in AM system implementation. The review concludes with future directions and research opportunities to enhance the environmental performance of AM technologies, ultimately contributing to a more sustainable manufacturing landscape. Full article

(This article belongs to the Special Issue High-Performance Metal Additive Manufacturing, 2nd Edition)

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31 pages, 17555 KB

Open AccessEditor’s ChoiceArticle

Evaluating Performance of Friction Stir Lap Welds Made at Ultra-High Speeds

by Todd Lainhart, Joshua Sheffield, Jeremy Russell, Jeremy Coyne and Yuri Hovanski

J. Manuf. Mater. Process. 2025, 9(8), 263; https://doi.org/10.3390/jmmp9080263 - 6 Aug 2025

Cited by 2 | Viewed by 2373

Abstract

Friction stir lap welding has been utilized across research and industry for over a decade. However, difficulties in welding in the lap configuration without an interface-related defect have prevented the process from moving beyond low feed rates (generally less than 1.5 m per [...] Read more.

Friction stir lap welding has been utilized across research and industry for over a decade. However, difficulties in welding in the lap configuration without an interface-related defect have prevented the process from moving beyond low feed rates (generally less than 1.5 m per minute). As a means of making a huge leap in welding productivity, this study will evaluate friction stir welds made at 10 m per minute (mpm), detailing the changes to tool geometries and weld parameters that result in fully consolidated welds. Characterization of the subsequent material properties, namely through optical microscopy, CT scanning, microhardness testing, tensile and fatigue testing, hermetic seal pressure tests, and electron backscattered diffraction, is presented as a means of demonstrating the quality and repeatability of friction stir lap welds made at 10 mpm. Fully consolidated welds were produced at spindle speeds 5.5% faster and 2.9% slower than nominal values and weld depths ranging from 1% shallower to 8.2% deeper than nominal values. Additionally, the loading direction of the weld had a significant impact on tensile properties, with the advancing side of the weld measured to be 16% stronger in lap-shear tensile and 289% fatigue life improvement under all loading conditions measured when compared to the retreating side. Full article

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23 pages, 20324 KB

Open AccessEditor’s ChoiceArticle

Hyperparameter Tuning of Artificial Neural Network-Based Machine Learning to Optimize Number of Hidden Layers and Neurons in Metal Forming

by Ebrahim Seidi, Farnaz Kaviari and Scott F. Miller

J. Manuf. Mater. Process. 2025, 9(8), 260; https://doi.org/10.3390/jmmp9080260 - 3 Aug 2025

Cited by 10 | Viewed by 3786

Abstract

Cold rolling is widely recognized as a key industrial process for enhancing the mechanical properties of materials, particularly hardness, through strain hardening. Despite its importance, accurately predicting the final hardness remains a challenge due to the inherently nonlinear nature of the deformation. While [...] Read more.

Cold rolling is widely recognized as a key industrial process for enhancing the mechanical properties of materials, particularly hardness, through strain hardening. Despite its importance, accurately predicting the final hardness remains a challenge due to the inherently nonlinear nature of the deformation. While several studies have employed artificial neural networks to predict mechanical properties, architectural parameters still need to be investigated to understand their effects on network behavior and model performance, ultimately supporting the design of more effective architectures. This study investigates hyperparameter tuning in artificial neural networks trained using Resilient Backpropagation by evaluating the impact of varying number of hidden layers and neurons on the prediction accuracy of hardness in 70-30 brass specimens subjected to cold rolling. A dataset of 1000 input–output pairs, containing dimensional and hardness measurements from multiple rolling passes, was used to train and evaluate 819 artificial neural network architectures, each with a different configuration of 1 to 3 hidden layers and 4 to 12 neurons per layer. Each configuration was tested over 50 runs to reduce the influence of randomness and enhance result consistency. Enhancing the network depth from one to two hidden layers improved predictive performance. Architectures with two hidden layers achieved better performance metrics, faster convergence, and lower variation than single-layer networks. Introducing a third hidden layer did not yield meaningful improvements over two-hidden-layer architectures in terms of performance metrics. While the top three-layer model converged in fewer epochs, it required more computational time due to increased model complexity and weight elements. Full article

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16 pages, 3072 KB

Open AccessEditor’s ChoiceArticle

Process Development to Repair Aluminum Components, Using EHLA and Laser-Powder DED Techniques

by Adrienn Matis, Min-Uh Ko, Richard Kraft and Nicolae Balc

J. Manuf. Mater. Process. 2025, 9(8), 255; https://doi.org/10.3390/jmmp9080255 - 31 Jul 2025

Cited by 3 | Viewed by 2892

Abstract

The article presents a new AM (Additive Manufacturing) process development, necessary to repair parts made from Aluminum 6061 material, with T6 treatment. The laser Directed Energy Deposition (DED) and Extreme High-Speed Directed Energy Deposition (EHLA) capabilities are evaluated for repairing Al large components. [...] Read more.

The article presents a new AM (Additive Manufacturing) process development, necessary to repair parts made from Aluminum 6061 material, with T6 treatment. The laser Directed Energy Deposition (DED) and Extreme High-Speed Directed Energy Deposition (EHLA) capabilities are evaluated for repairing Al large components. To optimize the process parameters, single-track depositions were analyzed for both laser-powder DED (feed rate of 2 m/min) and EHLA (feed rate 20 m/min) for AlSi10Mg and Al6061 powders. The cross-sections of single tracks revealed the bonding characteristics and provided laser-powder DED, a suitable parameter selection for the repair. Three damage types were identified on the Al component to define the specification of the repair process and to highlight the capabilities of laser-powder DED and EHLA in repairing intricate surface scratches and dents. Our research is based on variation of the powder mass flow and beam power, studying the influence of these parameters on the weld bead geometry and bonding quality. The evaluation criteria include bonding defects, crack formation, porosity, and dilution zone depth. The bidirectional path planning strategy was applied with a fly-in and fly-out path for the hatching adjustment and acceleration distance. Samples were etched for a qualitative microstructure analysis, and the HV hardness was tested. The novelty of the paper is the new process parameters for laser-powder DED and EHLA deposition strategies to repair large Al components (6061 T6), using AlSi10Mg and Al6061 powder. Our experimental research tested the defect-free deposition and the compatibility of AlSi10Mg on the Al6061 substrate. The readers could replicate the method presented in this article to repair by laser-powder DED/EHLA large Al parts and avoid the replacement of Al components with new ones. Full article

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14 pages, 1863 KB

Open AccessEditor’s ChoiceArticle

Advancements in Hole Quality for AISI 1045 Steel Using Helical Milling

by Pedro Mendes Silva, António José da Fonseca Festas, Robson Bruno Dutra Pereira and João Paulo Davim

J. Manuf. Mater. Process. 2025, 9(8), 256; https://doi.org/10.3390/jmmp9080256 - 31 Jul 2025

Cited by 2 | Viewed by 1830

Abstract

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, [...] Read more.

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

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21 pages, 4393 KB

Open AccessEditor’s ChoiceArticle

Lightweight and Sustainable Steering Knuckle via Topology Optimization and Rapid Investment Casting

by Daniele Almonti, Daniel Salvi, Emanuele Mingione and Silvia Vesco

J. Manuf. Mater. Process. 2025, 9(8), 252; https://doi.org/10.3390/jmmp9080252 - 24 Jul 2025

Cited by 15 | Viewed by 4219

Abstract

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing [...] Read more.

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing and manufacturing a MacPherson steering knuckle using topology optimization (TO), additive manufacturing, and rapid investment casting (RIC). Static structural simulations confirmed the mechanical integrity of the optimized design, with stress and strain values remaining within the elastic limits of the SG A536 iron alloy. The TO process achieved a 30% reduction in mass, resulting in lower material use and production costs. Additive manufacturing of optimized geometry reduced resin consumption by 27% and printing time by 9%. RIC simulations validated efficient mold filling and solidification, with porosity confined to removable riser regions. Life cycle assessment (LCA) demonstrated a 27% reduction in manufacturing environmental impact and a 31% decrease throughout the component life cycle, largely due to vehicle lightweighting. The findings highlight the potential of integrated TO and advanced manufacturing techniques to produce structurally efficient and environmentally sustainable automotive components. This workflow offers promising implications for broader industrial applications that aim to balance mechanical performance with ecological responsibility. Full article

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55 pages, 8888 KB

Open AccessEditor’s ChoiceArticle

Single, Multi-, and Many-Objective Optimization of Manufacturing Processes Using Two Novel and Efficient Algorithms with Integrated Decision-Making

by Ravipudi Venkata Rao and Joao Paulo Davim

J. Manuf. Mater. Process. 2025, 9(8), 249; https://doi.org/10.3390/jmmp9080249 - 22 Jul 2025

Cited by 8 | Viewed by 3625

Abstract

Manufacturing processes are inherently complex, multi-objective in nature, and highly sensitive to process parameter settings. This paper presents two simple and efficient optimization algorithms—Best–Worst–Random (BWR) and Best–Mean–Random (BMR)—developed to solve both constrained and unconstrained optimization problems of manufacturing processes involving single, multi-, and [...] Read more.

Manufacturing processes are inherently complex, multi-objective in nature, and highly sensitive to process parameter settings. This paper presents two simple and efficient optimization algorithms—Best–Worst–Random (BWR) and Best–Mean–Random (BMR)—developed to solve both constrained and unconstrained optimization problems of manufacturing processes involving single, multi-, and many-objectives. These algorithms are free from metaphorical inspirations and require no algorithm-specific control parameters, which often complicate other metaheuristics. Extensive testing reveals that BWR and BMR consistently deliver competitive, and often superior, performance compared to established methods. Their multi- and many-objective extensions, named MO-BWR and MO-BMR, respectively, have been successfully applied to tackle 2-, 3-, and 9-objective optimization problems in advanced manufacturing processes such as friction stir processing (FSP), ultra-precision turning (UPT), laser powder bed fusion (LPBF), and wire arc additive manufacturing (WAAM). To aid in decision-making, the proposed BHARAT can be integrated with MO-BWR and MO-BMR to identify the most suitable compromise solution from among a set of Pareto-optimal alternatives. The results demonstrate the strong potential of the proposed algorithms as practical tools for intelligent decision-making in real-world manufacturing applications. Full article

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18 pages, 2891 KB

Open AccessEditor’s ChoiceArticle

Size Effects on Process-Induced Porosity in Ti6Al4V Thin Struts Additively Manufactured by Laser Powder-Bed Fusion

by Nismath Valiyakath Vadakkan Habeeb and Kevin Chou

J. Manuf. Mater. Process. 2025, 9(7), 226; https://doi.org/10.3390/jmmp9070226 - 2 Jul 2025

Cited by 7 | Viewed by 3053

Abstract

Laser powder-bed fusion (L-PBF) additive manufacturing has been widely explored for fabricating intricate metallic parts such as lattice structures with thin struts. However, L-PBF-fabricated small parts (e.g., thin struts) exhibit different morphological and mechanical characteristics compared to bulk-sized parts due to distinct scan [...] Read more.

Laser powder-bed fusion (L-PBF) additive manufacturing has been widely explored for fabricating intricate metallic parts such as lattice structures with thin struts. However, L-PBF-fabricated small parts (e.g., thin struts) exhibit different morphological and mechanical characteristics compared to bulk-sized parts due to distinct scan lengths, affecting the melt pool behavior between transient and quasi-steady states. This study investigates the keyhole porosity in Ti6Al4V thin struts fabricated by L-PBF, incorporating a range of strut sizes, along with various levels of linear energy densities. Micro-scaled computed tomography and image analysis were employed for porosity measurements and evaluations. Generally, keyhole porosity lessens with decreasing energy density, though with varying patterns across a higher energy density range. Keyhole porosity in struts predictably becomes severe at high laser powers and/or low scan speeds. However, a major finding reveals that the porosity is reduced with decreasing strut size (if less than 1.25 mm diameter), plausibly because the keyhole formed has not reached a stable state to produce pores in a permanent way. This implies that a higher linear energy density, greater than commonly formulated in making bulk components, could be utilized in making small-scale features to ensure not only full melting but also minimum keyhole porosity. Full article

(This article belongs to the Special Issue Design, Processes and Materials for Additive Manufacturing: 2nd Edition)

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19 pages, 4360 KB

Open AccessEditor’s ChoiceArticle

A Feasibility Study on UV Nanosecond Laser Ablation for Removing Polyamide Insulation from Platinum Micro-Wires

by Danial Rahnama, Graziano Chila and Sivakumar Narayanswamy

J. Manuf. Mater. Process. 2025, 9(7), 208; https://doi.org/10.3390/jmmp9070208 - 21 Jun 2025

Cited by 1 | Viewed by 2269

Abstract

This study presents the optimization of a laser ablation process designed to achieve the precise removal of polyamide coatings from ultra-thin platinum wires. Removing polymer coatings is a critical challenge in high-reliability manufacturing processes such as aerospace thermocouple fabrication. The ablation process must [...] Read more.

This study presents the optimization of a laser ablation process designed to achieve the precise removal of polyamide coatings from ultra-thin platinum wires. Removing polymer coatings is a critical challenge in high-reliability manufacturing processes such as aerospace thermocouple fabrication. The ablation process must not only ensure the complete removal of the polyamide insulation but also maintain the tensile strength of the wire to withstand mechanical handling in subsequent manufacturing stages. Additionally, the exposed platinum surface must exhibit low surface roughness to enable effective soldering and be free of thermal damage or residual debris to pass strict visual inspections. The wires have a total diameter of 65 µm, consisting of a 50 µm platinum core encased in a 15 µm polyamide coating. By utilizing a UV laser with a wavelength of 355 nm, average power of 3 W, a repetition rate range of 20 to 200 kHz, and a high-speed marking system, the process parameters were systematically refined. Initial attempts to perform the ablation in an air medium were unsuccessful due to inadequate thermal control and incomplete removal of the polyamide coating. Hence, a water-assisted ablation technique was explored to address these limitations. Experimental results demonstrated that a scanning speed of 1200 mm/s, coupled with a line spacing of 1 µm and a single ablation pass, resulted in complete coating removal while ensuring the integrity of the platinum substrate. The incorporation of a water layer above the ablation region was considered crucial for effective heat dissipation, preventing substrate overheating and ensuring uniform ablation. The laser’s spot diameter of 20 µm in air and a focal length of 130 mm introduced challenges related to overlap control between successive passes, requiring precise calibration to maintain consistency in coating removal. This research demonstrates the feasibility and reliability of water-assisted laser ablation as a method for a high-precision, non-contact coating material. Full article

(This article belongs to the Special Issue Advances in Laser-Assisted Manufacturing Techniques)

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23 pages, 4160 KB

Open AccessEditor’s ChoiceArticle

Computed Tomography-Based Volumetric Additive Manufacturing: Development of a Model Based on Resin Properties and Part Size Interrelationship—Part I

by Amir H. Behravesh, Asra Tariq, John Buni and Ghaus Rizvi

J. Manuf. Mater. Process. 2025, 9(6), 178; https://doi.org/10.3390/jmmp9060178 - 28 May 2025

Cited by 3 | Viewed by 2132

Abstract

This study presents an analytical description of the computed tomography-based volumetric additive manufacturing (VAM) process, with an emphasis on the impact of resin properties on product dimensions. The main issue addressed in this study is the assessment of the dimensional limitation of the [...] Read more.

This study presents an analytical description of the computed tomography-based volumetric additive manufacturing (VAM) process, with an emphasis on the impact of resin properties on product dimensions. The main issue addressed in this study is the assessment of the dimensional limitation of the objects produced using the VAM process, which is usually reported to be of the order of one centimeter. An analytical model is introduced to predict the product size based on the resin property (penetration depth—D_p), vial size (radius), and the duration of part formation, and the results indicate significant correlations among these parameters. A method of D_p measurement and analysis that is appropriate for the VAM process is also introduced. Mathematical justification is provided along with experimental verification for the effects of the main governing factor, D_p, on the maximum possible product size. Multiple criteria are also introduced for selecting the appropriate size of the resin container (vial) based on the desired object size and the value of D_p. It was found that the D_p is a crucial factor in analysis and experimentation in the VAM process, and its value is fundamentally different from the one obtained in the conventional polymerization AM methods. The product dimension based on the resin property, vial size, and time for the formation of the part is introduced by the analytical model. This model provides valuable insights into the complex interplay of factors influencing VAM outcomes and can facilitate informed decision-making in material selection and process design. Full article

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12 pages, 4178 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Conditions for Self-Healing of Additively Manufactured Polymer Composites with Continuous Carbon Fiber Reinforcement

by Marius Rimašauskas, Tomas Kuncius, Rūta Rimašauskienė and Tomas Simokaitis

J. Manuf. Mater. Process. 2025, 9(6), 179; https://doi.org/10.3390/jmmp9060179 - 28 May 2025

Cited by 3 | Viewed by 2446

Abstract

Additive manufacturing (AM) is one of the most frequently used technologies to produce complex configuration products. Moreover, AM is very well known as a technology which is characterized by a low amount of generated waste and the potential to be called zero-waste technology. [...] Read more.

Additive manufacturing (AM) is one of the most frequently used technologies to produce complex configuration products. Moreover, AM is very well known as a technology which is characterized by a low amount of generated waste and the potential to be called zero-waste technology. As is known, there are seven main groups of technologies described in the ISO/ASTM 52900 standard that allow the use of very different materials from polymers to metals, ceramics, and composites. However, the increased utilization of additively manufactured composites for different applications requires a deeper analysis of production processes and materials’ characteristics. Various AM technologies can be used to produce complex composite structures reinforced with short fibers; however, only material extrusion (MEX)-based technology is used for the production of composites reinforced with continuous fibers (CFs). At this time, five different methods exist to produce CF-reinforced composite structures. This study focuses on co-extrusion with the towpreg method. Because of the complexity and layer-by-layer nature of the process, defects can occur during production, such as poor interlayer adhesion, increased porosity, insufficient impregnation, and others. To eliminate or minimize defects’ influence on mechanical properties and structural integrity of additively manufactured structures, a hypothesis was proposed involving heat treatment. Carbon fiber’s conductive properties can be used to heal the composite structures, by heating them up through the application of electric current. In this research article, an experimental evaluation of conditions for additively manufactured composites with continuous carbon fiber reinforcement for self-healing processes is presented. Mechanical testing was conducted to check the influence of heat treatment on the flexural properties of the composite samples. Full article

(This article belongs to the Special Issue Innovative and Sustainable Advances in Polymer Composites for Additive Manufacturing: Processing, Microstructure, Machining, and Mechanical Properties)

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13 pages, 4249 KB

Open AccessEditor’s ChoiceArticle

The First Layer: Single-Track Insights into Direct Energy Deposition Processed Cu-Ni Thermoelectric Alloys

by Nick Williams, Kyle Snyder, Ian Smith, Anthony Duong, Everett Carpenter and Radhika Barua

J. Manuf. Mater. Process. 2025, 9(6), 170; https://doi.org/10.3390/jmmp9060170 - 23 May 2025

Cited by 3 | Viewed by 2651

Abstract

The shift to sustainable energy has accelerated the development of thermoelectric (TE) material for direct heat-to-electricity conversion without batteries or grid reliance. Cu-Ni alloys show promise for high-power, thermally stable TE applications like waste heat recovery and electronics cooling but require thermal conductivity [...] Read more.

The shift to sustainable energy has accelerated the development of thermoelectric (TE) material for direct heat-to-electricity conversion without batteries or grid reliance. Cu-Ni alloys show promise for high-power, thermally stable TE applications like waste heat recovery and electronics cooling but require thermal conductivity and microstructure optimization. This study investigates additive manufacturing (AM) of Cu-Ni alloys via laser powder-directed energy deposition (L-DED), enabling precise control over deposition parameters. Track geometries were analyzed using linear mass density (M_L) and linear heat input (H_L), which influence deposition quality and microstructural characteristics. A weighted qualitative process parameter decision matrix was developed to evaluate process conditions systematically. Optimal deposition was achieved with H_L < 70 J/mm for M_L ~0.016–0.021 g/mm and 98 J/mm < H_L < 137 J/mm for M_L = 0.026 g/mm, corresponding to an energy-to-mass ratio of ~4000 ± 500 kJ/g. While this study does not directly assess thermoelectric properties, it provides essential first-layer insights into how processing conditions affect track geometry, defect formation, and microstructure—information that is foundational for optimizing multi-layer builds and, ultimately, improving thermoelectric performance. These findings mark a critical step toward predictive process optimization and the accelerated design of Cu-Ni-based thermoelectric materials using AM techniques. Full article

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20 pages, 10342 KB

Open AccessEditor’s ChoiceArticle

Investigation of the Thermo-Mechanical Modeling of the Manufacturing of Large-Scale Wire Arc Additive Manufacturing Components with an Outlook Towards Industrial Applications

by Tim Fritschle, Moritz Kaess, Stefan Weihe and Martin Werz

J. Manuf. Mater. Process. 2025, 9(5), 166; https://doi.org/10.3390/jmmp9050166 - 20 May 2025

Cited by 8 | Viewed by 3563

Abstract

The simulation of additive manufacturing processes, such as Wire Arc Additive Manufacturing (WAAM), is becoming increasingly important to predict material and component properties in advance of the real-life manufacturing. In contrast to prior work focusing on the simulation of simplified WAAM parts, this [...] Read more.

The simulation of additive manufacturing processes, such as Wire Arc Additive Manufacturing (WAAM), is becoming increasingly important to predict material and component properties in advance of the real-life manufacturing. In contrast to prior work focusing on the simulation of simplified WAAM parts, this paper presents an investigation into the thermo-mechanical finite element (FE) simulation of the manufacturing of large-scale WAAM components. The investigation focuses on various problems within the individual steps of the FE workflow wherein ABAQUS influences the modeling of large-scale components. The investigations are founded upon a thermo-mechanically coupled FE model in ABAQUS 2020. For this purpose, several thermo-mechanical simulation models are set up with the target of investigating the meshing, element activation and variation of process parameters. Appropriate discretization of WAAM components is found to be a major problem when setting up a simulation. The meshing of the component is limited by the element type and size and the meshing routines used. Also, differences in the axes of motion for the simulation and the real process cause the simulation to differ from reality. High element start temperatures are found to be beneficial for simulation stability and performance. An integrated parameter variation was made possible with the modeling techniques used. Full article

(This article belongs to the Special Issue Large-Scale Metal Additive Manufacturing)

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18 pages, 8376 KB

Open AccessEditor’s ChoiceArticle

Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling

by Jiduo Zhang, Robert Heinemann and Otto Jan Bakker

J. Manuf. Mater. Process. 2025, 9(5), 160; https://doi.org/10.3390/jmmp9050160 - 14 May 2025

Cited by 2 | Viewed by 1589

Abstract

In drilling Carbon-Fibre-Reinforced Polymers (CFRP)/Al stacks, adaptive drilling facilitates the optimisation of cutting parameters for each constituent stack layer and tool wear, thus enhancing cutting efficiency and borehole quality. This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both process [...] Read more.

In drilling Carbon-Fibre-Reinforced Polymers (CFRP)/Al stacks, adaptive drilling facilitates the optimisation of cutting parameters for each constituent stack layer and tool wear, thus enhancing cutting efficiency and borehole quality. This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both process incidences and tool wear in the drilling of hybrid stacks, which subsequently informs the machine tool to adjust cutting parameters or, if necessary, replaces the tool. TPP is introduced to remove the restriction of input dimensions, allowing for the acceptance of inputs with arbitrary shapes. On the other hand, a knot structure has been proposed to incorporate the classification of process incidences into the tool wear analysis, thereby enhancing prediction accuracy. The proposed model achieves a process incidence identification accuracy of 99.19% and a Mean Absolute Error (MAE) of 10 μm in tool wear prediction, demonstrating robust performance across a wide range of sampling conditions. This achievement facilitates decision-making and optimisation relating to cutting parameters and tool replacement in the context of adaptive drilling of aerospace materials. Full article

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22 pages, 3812 KB

Open AccessEditor’s ChoiceArticle

Dynamic Dwell Time Adjustment in Wire Arc-Directed Energy Deposition: A Thermal Feedback Control Approach

by Md Munim Rayhan, Abderrachid Hamrani, Fuad Hasan, Tyler Dolmetsch, Arvind Agarwal and Dwayne McDaniel

J. Manuf. Mater. Process. 2025, 9(5), 143; https://doi.org/10.3390/jmmp9050143 - 27 Apr 2025

Cited by 6 | Viewed by 3455

Abstract

Precise thermal management remains a critical challenge in Wire Arc-Direct Energy Deposition (W-DED) processes due to significant temperature fluctuations that can adversely impact part quality, dimensional accuracy, and process reliability. To address these issues, this study introduces a novel Hybrid Interlayer Hysteresis Controller [...] Read more.

Precise thermal management remains a critical challenge in Wire Arc-Direct Energy Deposition (W-DED) processes due to significant temperature fluctuations that can adversely impact part quality, dimensional accuracy, and process reliability. To address these issues, this study introduces a novel Hybrid Interlayer Hysteresis Controller (HIHC) designed specifically for W-DED, which integrates real-time thermal feedback and adaptive dwell time control. The system implements a dual-mode cooling strategy based on a temperature threshold, utilizing optical character recognition-based temperature monitoring and a rolling buffer system for stability. Experimental validation demonstrated improvements in thermal management, with the dynamic control system maintaining an average temperature undershoot of 1.38% while achieving 96.29% optimal temperature window compliance. Surface quality analysis revealed an 8.67% improvement in front face smoothness and a 5.15% enhancement in top surface quality. The dynamic control system also exhibited superior dimensional accuracy, producing thin walls with widths of 61.98 mm versus 66.43 mm in fixed dwell time samples, relative to the intended 60 mm specification. This study advances the field of additive manufacturing by establishing a robust framework for precise thermal management in W-DED processes, contributing to enhanced part quality, reduced post-processing requirements, and improved process reliability. Despite these advances, limitations include the system’s dependence on external optical monitoring hardware, potential scalability constraints for complex geometries, and limited testing across diverse material systems. Future work should focus on integrating multi-axis thermal sensors, extending the framework to multi-material deposition scenarios and implementing machine learning algorithms for predictive thermal modeling. Full article

(This article belongs to the Special Issue Advances in Directed Energy Deposition Additive Manufacturing)

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