Journal of Manufacturing and Materials Processing

27 pages, 6905 KB

Open AccessArticle

Effect of Laser Scanning Parameters on Topography and Morphology of Femtosecond Laser-Structured Hot-Work Tool Steel Surfaces

by Robert Thomas, Hermann Seitz and Georg Schnell

J. Manuf. Mater. Process. 2026, 10(2), 58; https://doi.org/10.3390/jmmp10020058 - 7 Feb 2026

Abstract

In mechanical engineering, interest in reliable and practicable technologies for nano- and microstructuring of tool surfaces is increasing. Femtosecond laser structuring offers a promising approach that combines high processing speeds with high precision. However, a knowledge gap remains regarding the optimal process parameters [...] Read more.

In mechanical engineering, interest in reliable and practicable technologies for nano- and microstructuring of tool surfaces is increasing. Femtosecond laser structuring offers a promising approach that combines high processing speeds with high precision. However, a knowledge gap remains regarding the optimal process parameters for achieving specific surface patterns on hot-work tool steel substrates. The current study aims to investigate the effects of laser scanning parameters on the formation of self-organized surface structures and the resulting topography and morphology. Therefore, samples were irradiated using a 300 fs laser with linearly polarized light (λ = 1030 nm). Scanning electron microscopy revealed four structure types: laser-induced periodic surface structures (LIPSSs), micrometric ripples, micro-crater structures, and pillared microstructures. The results for surface area and line roughness indicate that high laser pulse overlaps lower the strong ablation threshold more effectively than high scanning line overlaps, promoting the formation of pillared microstructures. For efficient ablation and increased surface roughness, higher pulse overlaps are therefore advantageous. In contrast, at low fluences, higher scanning line overlaps support a more homogeneous formation of nanostructures and reduce waviness. Full article

(This article belongs to the Special Issue Advanced Laser-Assisted Manufacturing Processes)

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Graphical abstract

18 pages, 2466 KB

Open AccessArticle

Prediction Model of Dynamic Error for Ultra-Precision Vertical Grinding System

by Mengyang Li, Jiasheng Li and Ming Huang

J. Manuf. Mater. Process. 2026, 10(2), 57; https://doi.org/10.3390/jmmp10020057 - 6 Feb 2026

Abstract

Ultra-precision grinding is widely used in fields such as precision instrumentation, military industry, and aerospace. Focusing on a grinding system based on hydrostatic support, this paper investigates the formation mechanism and variation patterns of roundness error during grinding. The dynamic equations are derived [...] Read more.

Ultra-precision grinding is widely used in fields such as precision instrumentation, military industry, and aerospace. Focusing on a grinding system based on hydrostatic support, this paper investigates the formation mechanism and variation patterns of roundness error during grinding. The dynamic equations are derived based on the structural characteristics of a vertical grinding system. The uncut chip thickness is formulated, enabling the prediction of grinding forces through mathematical expressions. The dynamic equations are solved using a fully discrete algorithm to obtain the surface profile of the workpiece after machining, and roundness error is extracted using the least squares method. As the speed ratio of the grinding wheel to the workpiece increases, the grinding accuracy improves, and the roundness error can be controlled within 0.2 μm. The farther the grinding force application point is from the center of the slider, the greater the roundness error. Under the condition of meeting the processing range, a shorter grinding wheel contact rod should be selected. Full article

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19 pages, 2969 KB

Open AccessArticle

Spine Motion Segment Analogues: 3D Printing, Multiscale Modelling and Testing to Produce More Biofidelic Examples

by Constantinos Franceskides, Tobias Shanker, Michael C. Gibson and Peter Zioupos

J. Manuf. Mater. Process. 2026, 10(2), 56; https://doi.org/10.3390/jmmp10020056 - 6 Feb 2026

Abstract

Computed tomography and magnetic resonance imaging are two powerful modalities which can be used in the clinical setting to produce data for the creation of patient-specific finite element analysis (FEA) models and physical analogues—for instance, by using additive manufacturing (AM)—that mimic the properties [...] Read more.

Computed tomography and magnetic resonance imaging are two powerful modalities which can be used in the clinical setting to produce data for the creation of patient-specific finite element analysis (FEA) models and physical analogues—for instance, by using additive manufacturing (AM)—that mimic the properties of soft and hard tissues, both morphologically and mechanically. However, there remains a gap between creating a perfect biofidelic physical analogue and its computational counterpart. This gap exists because, firstly, in silico models are often too complex to realise, and secondly, real-life conditions are challenging to emulate both computationally and mechanically, as they involve multiscale situations that are inherently heterogeneous and patient specific. In this study, we applied a multi-scale approach to design and model porcine vertebral specimens. Our results identified critical design factors that affect the quality and accuracy of the models, specifically highlighting that scanning resolution/fidelity and the thresholding technique have a directly proportional impact on model accuracy. A small shift up and down the greyscale level by 20 units can affect the behaviour of the AM sample by as much as [−15% +47%]. Working up the levels for manufacturing, testing and modelling (i) cylindrical cores to (ii) whole vertebrae and then (iii) a whole spine motion segment, we observed that the fidelity of predictions reduces, and errors increase as the structure becomes more complicated and intricate (3.6%, 7.5% and 15%, respectively). We are confident that further material-level developments will provide solutions for the more intricate parts of spinal motion segments, such as the intervertebral discs and facets, which in their natural form are highly sophisticated structures. To the best of our knowledge, this is the first time a holistic multiscale approach has been implemented to produce AM biofidelic analogues of skeletal parts. Our data showed good agreement between the physical and in silico models, confirming that it is possible to model real-time objects and situations both physically and in silico. This ultimately will enable the development of accurate, patient-specific physical models for use in biomechanical testing and medicolegal applications. Full article

(This article belongs to the Special Issue Recent Advances in 3D Printing Technologies in Bioengineering with Selected Papers from the 29th Congress of the European Society of Biomechanics (ESB 2024))

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22 pages, 4864 KB

Open AccessArticle

A K-Means Clustering Approach for Accelerated Path Planning in GMA-DED: The Fast Advanced-Pixel Strategy

by Rafael P. Ferreira, Vinicius Lemes Jorge, Emil Schubert and Américo Scotti

J. Manuf. Mater. Process. 2026, 10(2), 55; https://doi.org/10.3390/jmmp10020055 - 5 Feb 2026

Abstract

The performance of Gas Metal Arc-Directed Energy Deposition (GMA-DED) strongly depends on efficient path-planning strategies that balance trajectory quality and computational cost. With the purpose of developing a computationally faster and more scalable path-planning approach, this study introduces the Fast Advanced-Pixel strategy by [...] Read more.

The performance of Gas Metal Arc-Directed Energy Deposition (GMA-DED) strongly depends on efficient path-planning strategies that balance trajectory quality and computational cost. With the purpose of developing a computationally faster and more scalable path-planning approach, this study introduces the Fast Advanced-Pixel strategy by integrating the K-means clustering algorithm into to the Advanced Pixel strategy version to reduce the dimensionality of an optimization problem. Computational validation was conducted on four geometrically distinct parts using different clustering configurations. Statistical analysis (ANOVA) was applied to assess the significance of the results. The findings revealed that by increasing the number of clusters, computational time is substantially reduced, achieving up to a twenty-fold improvement compared with the former strategy, while maintaining consistent trajectory quality. Experimental validation using complex parts, such as a “Jaw Gripper” and a “C-frame” of a resistance spot welding gun, confirmed defect-free deposition and dimensional agreement with the CAD models. Accordingly, within the scope of GMA-DED technology and pixel-based path-planning strategies, the Fast Advanced-Pixel approach demonstrates a significant improvement in computational efficiency while preserving trajectory quality, enabling the accurate and reliable fabrication of geometrically complex metallic parts. Full article

(This article belongs to the Special Issue Arc-Based Directed Energy Deposition: Processes, Applications and Monitoring)

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10 pages, 1613 KB

Open AccessArticle

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

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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22 pages, 15286 KB

Open AccessArticle

Effect of Precise TIG Welding Pool Temperature Control on Microstructure and Mechanical Properties of 7072 Aluminum Alloy Joints

by Yan Wang, Yang Li, Wenhui Zhang, Yonglin Zhao and Chao Liu

J. Manuf. Mater. Process. 2026, 10(2), 53; https://doi.org/10.3390/jmmp10020053 - 1 Feb 2026

Abstract

This study investigates the effect of TIG weld pool temperature on the microstructure and mechanical properties of crack-repaired joints in 7072 aluminum alloy. To address poor temperature stability and slow response in indirect temperature control during TIG welding by adjusting the parameters, a [...] Read more.

This study investigates the effect of TIG weld pool temperature on the microstructure and mechanical properties of crack-repaired joints in 7072 aluminum alloy. To address poor temperature stability and slow response in indirect temperature control during TIG welding by adjusting the parameters, a new closed-loop molten pool temperature control method is proposed. Experimental comparisons were conducted to evaluate its effectiveness. The results show that using real-time molten pool temperature as direct feedback allows the welding current to be adjusted dynamically. This approach enables precise control of heat input. It also achieves real-time tracking and stable regulation of the molten pool temperature during welding. Temperature-controlled welding yields a more uniform joint microstructure with reduced porosity. Notably, the joint exhibits optimal comprehensive mechanical properties at 1825 °C, with a tensile strength of 328 MPa and an elongation of 9.9%. Overall, precise control of the TIG weld pool temperature effectively improves the quality and performance uniformity of aluminum alloy crack repairs. Full article

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23 pages, 3101 KB

Open AccessArticle

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

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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24 pages, 374 KB

Open AccessReview

Recycled Stainless Steel as a Sustainable Feedstock for Direct Metal Laser Sintering: Challenges and Opportunities

by Shubham Chaudhry and Amy Hsiao

J. Manuf. Mater. Process. 2026, 10(2), 51; https://doi.org/10.3390/jmmp10020051 - 31 Jan 2026

Abstract

Direct metal laser sintering (DMLS) is an advanced powder bed fusion (PBF) technology widely utilized in the medical device and aerospace sectors for the production of intricate and high-value components. The powdered metal materials used in the DMLS process can be expensive, and [...] Read more.

Direct metal laser sintering (DMLS) is an advanced powder bed fusion (PBF) technology widely utilized in the medical device and aerospace sectors for the production of intricate and high-value components. The powdered metal materials used in the DMLS process can be expensive, and it is uncommon for a single build to exhaust an entire batch of powder. As a result, the un-melted powder characterized by differences in particle size and morphology compared to fresh virgin powder is recommended to be recycled for use in subsequent builds. This comprehensive review delves into the essential role that powder quality plays in the realm of DMLS with a particular focus on effective and sustainable powder recycling strategies. In this study, the effects of recycling stainless steel powder, specifically used in the DMLS process, are rigorously investigated in relation to the quality of the finished components. This paper monitors critical powder material characteristics, including particle size, particle morphology, and the overall bulk chemical composition throughout the recycling workflow. Furthermore, this review brings to light significant challenges associated with the recycling of stainless steel powders, such as the need to maintain consistency in particle size and shape, manage contamination risks, and mitigate the degradation effects that can arise from repeated usage, including wear, fragmentation, and oxidation of the particles. In addition, this paper explores a variety of recycling techniques aimed at rejuvenating powder quality. These techniques, including sieving, blending, and plasma spheroidization, are emphasized for their vital role in restoring the integrity of recycled powders and facilitating their reuse in innovative and efficient manufacturing processes. Full article

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

13 pages, 1874 KB

Open AccessArticle

Effects of Process Parameters, Sheet Thickness and Adhesive on Spot Diameter During Resistance Spot Welding of Aluminum Alloys EN AW-5182 and EN AW-6005

by Andreas Fezer, Stefan Weihe and Martin Werz

J. Manuf. Mater. Process. 2026, 10(2), 50; https://doi.org/10.3390/jmmp10020050 - 31 Jan 2026

Abstract

Resistance spot welding (RSW) is one of the dominant joining processes in body-in-white manufacturing within the automotive industry, while the use of aluminum alloys continues to increase. This study investigates the influence of key process parameters on the spot diameter in RSW of [...] Read more.

Resistance spot welding (RSW) is one of the dominant joining processes in body-in-white manufacturing within the automotive industry, while the use of aluminum alloys continues to increase. This study investigates the influence of key process parameters on the spot diameter in RSW of the aluminum alloys EN AW-5182 (AL5-STD) and EN AW-6005 (AL6-HDI). Experiments were performed using industry-standard robotic welding equipment in a partially automated welding cell. Welding current, electrode force, sheet thickness (1–3 mm), and adhesive application were systematically varied. The welded joints were evaluated by destructive testing to determine spot diameter. The results show that higher welding currents increase the spot diameter for both alloys, while higher electrode forces decrease it. EN AW-5182 exhibited a high tendency toward expulsion, whereas no expulsions occurred for EN AW-6005 under identical conditions. The application of the structural adhesive BETAMATE™ 1640 consistently increased the spot diameter. Full article

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16 pages, 5186 KB

Open AccessArticle

A FEM-ML Hybrid Framework for Optimizing the Cooling Schedules of Roll-Bonded Clad Plates

by Alexey G. Zinyagin, Alexander V. Muntin, Nikita R. Borisenko, Andrey P. Stepanov and Maria O. Kryuchkova

J. Manuf. Mater. Process. 2026, 10(2), 49; https://doi.org/10.3390/jmmp10020049 - 30 Jan 2026

Abstract

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims [...] Read more.

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims to develop an optimal cooling schedule for a 25 mm thick clad plate, comprising a X70-grade steel base layer and an AISI 316L cladding, to ensure required strength and minimal bending. A comprehensive approach was employed, integrating a 3D finite element model (Ansys) for simulating thermoelastic stresses with a CatBoost machine learning model trained on industrial data to predict heat transfer coefficients accurately. A parametric analysis of cooling strategies was conducted. Results showed that a standard cooling strategy caused unacceptable bending of plate after cooling exceeding 130 mm. An optimized strategy featuring delayed activation of the lower cooling headers (on the cladding side) created a compensating thermoelastic moment, successfully reducing bending to approximately 20 mm while maintaining the base layer’s requisite mechanical properties. The findings validate the efficacy of the combined FEM-machine learning methodology and propose a viable, industrially implementable cooling strategy for high-quality clad plate production. Full article

(This article belongs to the Special Issue Recent Developments in Materials Processing for Modern Applications: Advancements and Challenges)

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1 pages, 147 KB

Open AccessCorrection

Correction: Barmouz et al. Long-Term Impact of Sterilization Cycles on the Surface and Mechanical Integrity of Medical-Grade Silicone. J. Manuf. Mater. Process. 2025, 9, 282

by Mohsen Barmouz, Bahman Azarhoushang, Wolfram Kintzel and Volker Bucher

J. Manuf. Mater. Process. 2026, 10(2), 48; https://doi.org/10.3390/jmmp10020048 - 29 Jan 2026

Abstract

In the original publication [...] Full article

20 pages, 7285 KB

Open AccessArticle

Numerical Assessment on the Mixer in the Combined Application of Intensive Melt Shearing and Magnetic Field

by Jinchuan Wang, Yubo Zuo, Cheng Lin, Fei Li and Jinye Yao

J. Manuf. Mater. Process. 2026, 10(2), 47; https://doi.org/10.3390/jmmp10020047 - 28 Jan 2026

Abstract

This paper examines the combined effects of intensive melt shearing and magnetic fields on the flow characteristics of molten aluminum alloy during the direct chill (DC) casting process. Using computational fluid dynamics (CFD), we analyze flow fields across varying rotor speeds, diameters, blade [...] Read more.

This paper examines the combined effects of intensive melt shearing and magnetic fields on the flow characteristics of molten aluminum alloy during the direct chill (DC) casting process. Using computational fluid dynamics (CFD), we analyze flow fields across varying rotor speeds, diameters, blade counts, stator configurations, and electromagnetic field strengths. The results demonstrate that increasing rotor speed and diameter significantly enhances melt flow velocity and vortex intensity. Conversely, a greater number of rotor blades results in reduced flow velocity due to higher resistance. Moreover, the shape and diameter of the stator opening impact flow patterns, with circular openings producing higher velocities. The interaction of electromagnetic fields and intensive shearing generates three distinct vortices, achieving the highest overall flow velocity and a uniform distribution. Additionally, adjustments in excitation coil current and rotor speed enable further control of melt flow, offering valuable insights for optimizing casting processes and enhancing ingot quality. Full article

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39 pages, 3325 KB

Open AccessArticle

Novel Middleware Framework for Integrating Extended Reality into Robotic Manufacturing Processes

by Zoltán Szilágyi, Csaba Hajdu, Károly Széll and Péter Galambos

J. Manuf. Mater. Process. 2026, 10(2), 46; https://doi.org/10.3390/jmmp10020046 - 27 Jan 2026

Abstract

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture [...] Read more.

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments. Full article

(This article belongs to the Special Issue Robotics in Manufacturing Processes)

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20 pages, 2491 KB

Open AccessArticle

The Development and Validation of Equipment for Determining Friction and Adhesion Coefficients Under Conditions Comparable to the Injection Moulding Process

by Ângela R. Rodrigues, Mário S. Correia and António J. Pontes

J. Manuf. Mater. Process. 2026, 10(2), 45; https://doi.org/10.3390/jmmp10020045 - 27 Jan 2026

Abstract

Despite extensive research, predicting ejection forces during the injection moulding process remains challenging due to the complex calculation of the coefficient of friction. This coefficient comprises various components that are difficult to isolate, complicating independent predictions. Consequently, using literature-documented coefficient of friction for [...] Read more.

Despite extensive research, predicting ejection forces during the injection moulding process remains challenging due to the complex calculation of the coefficient of friction. This coefficient comprises various components that are difficult to isolate, complicating independent predictions. Consequently, using literature-documented coefficient of friction for different material contacts often leads to discrepancies between the experimental data and the predictive models of ejection forces. The adhesion component is a particularly problematic aspect, and is frequently excluded from analyses due to the difficulties associated with measuring it directly. This study aims to present the development of a tool designed to measure both the friction and adhesion forces under conditions that replicate those of the injection moulding process. The measured values were then used to calculate the corresponding coefficients: the static coefficient of friction (COF) and the coefficient of adhesion (COA). The conducted measurements identified the most influential factors in both coefficient values using a Design of Experiments (DoE), revealing that replication temperature was the factor promoting the highest variation across all the tested polymers. Full article

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

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Graphical abstract

30 pages, 6969 KB

Open AccessArticle

Machine Learning for In Situ Quality Assessment and Defect Diagnosis in Refill Friction Stir Spot Welding

by Jordan Andersen, Taylor Smith, Jared Jackson, Jared Millett and Yuri Hovanski

J. Manuf. Mater. Process. 2026, 10(2), 44; https://doi.org/10.3390/jmmp10020044 - 27 Jan 2026

Abstract

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence [...] Read more.

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

(This article belongs to the Special Issue Born Qualified Advanced Manufacturing—Modeling, Monitoring, Control, and AI for Rapid Part Quality Qualification in Advanced Manufacturing Processes)

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17 pages, 2454 KB

Open AccessArticle

Ratcheting Assessment of Medium Carbon and Austenitic Steel Alloys at Elevated Temperatures

by Petar Jevtic and Ahmad Varvani-Farahani

J. Manuf. Mater. Process. 2026, 10(2), 43; https://doi.org/10.3390/jmmp10020043 - 25 Jan 2026

Abstract

The present study intends to evaluate the ratcheting of ER9 wheel medium carbon steel and austenitic steel samples at room and elevated temperatures subjected to uniaxial loading cycles through the use of the Ahmadzadeh–Varvani (A–V) kinematic hardening rule. The A–V framework incorporated an [...] Read more.

The present study intends to evaluate the ratcheting of ER9 wheel medium carbon steel and austenitic steel samples at room and elevated temperatures subjected to uniaxial loading cycles through the use of the Ahmadzadeh–Varvani (A–V) kinematic hardening rule. The A–V framework incorporated an exponential function in the dynamic recovery term to account for the dynamic strain aging (DSA) phenomenon at temperatures where solute atoms and moving dislocations showed increased interaction. Within the DSA domain at 573K for ER9 wheel steel samples, and at 423K for austenitic steel samples, the collision of carbon and nitrogen solute atoms with moving dislocations resulted in the materials hardening, and promoted the yield strength. The Voyiadjis–Song–Rusinek (VSR) multivariable model was used to capture the evolution of yield strength with temperature. The predicted ratcheting results within the DSA temperature domain were in close agreement with those of measured values. Full article

(This article belongs to the Special Issue Deformation and Mechanical Behavior of Metals and Alloys)

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18 pages, 14911 KB

Open AccessArticle

A Library of Mechanical Properties of Cu-CuAl Alloys Produced by Wire and Arc Additive Manufacturing

by Filipa G. Cunha, Beatriz Nunes, Valdemar Duarte, Telmo G. Santos and José Xavier

J. Manuf. Mater. Process. 2026, 10(1), 42; https://doi.org/10.3390/jmmp10010042 - 22 Jan 2026

Abstract

This study aims to develop a library of Cu-CuAl material compositions and evaluate their mechanical properties. Various compositions are fabricated using Wire Arc Additive Manufacturing (WAAM) with GMAW and GTAW processes. The produced materials are characterised through hardness testing, eddy current measurements, and [...] Read more.

This study aims to develop a library of Cu-CuAl material compositions and evaluate their mechanical properties. Various compositions are fabricated using Wire Arc Additive Manufacturing (WAAM) with GMAW and GTAW processes. The produced materials are characterised through hardness testing, eddy current measurements, and tensile testing supported by Digital Image Correlation (DIC). The hardness analysis reveals that increasing the CuAl content leads to higher hardness values. All compositions display stable and consistent eddy current measurements, except for the alloy with 25% Cu and 75% CuAl, which shows comparatively higher values. The load–displacement curves indicate that higher Cu content enhances ductility, resulting in a lower maximum load. Conversely, a higher CuAl fraction is directly associated with greater ultimate tensile strength. Overall, compositions with higher CuAl content exhibit improved mechanical performance, although they do not reach the levels of commercial materials due to defects inherent to the additive manufacturing process. Full article

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14 pages, 4223 KB

Open AccessArticle

Fabrication of Highly Sensitive Conformal Temperature Sensors on Stainless Steel via Aerosol Jet Printing

by Ziqi Wang, Jun Xu, Yingjie Niu, Yuanyuan Tan, Biqi Yang and Chenglin Yi

J. Manuf. Mater. Process. 2026, 10(1), 41; https://doi.org/10.3390/jmmp10010041 - 21 Jan 2026

Abstract

Promoting the development of aerospace vehicles toward structural–functional integration and intelligent sensing is a key strategy for achieving lightweight, high-reliability, and autonomous operation and maintenance of next-generation aircraft. However, traditional external sensors face significant limitations because of their bulky size, installation challenges, and [...] Read more.

Promoting the development of aerospace vehicles toward structural–functional integration and intelligent sensing is a key strategy for achieving lightweight, high-reliability, and autonomous operation and maintenance of next-generation aircraft. However, traditional external sensors face significant limitations because of their bulky size, installation challenges, and incompatibility with aerodynamic surfaces. These issues are particularly pronounced on complex, high-curvature substrates, where achieving conformal bonding is difficult, thus restricting their application in critical components. In this study, aerosol jet printing (AJP) was employed to directly fabricate silver nanoparticle-based temperature sensors with real-time monitoring capabilities on the surface of high-curvature stainless steel sleeves, which serve as typical engineering components. This approach enables the in situ manufacturing of high-precision conformal sensors. Through optimized structural design and thermal treatment, the sensors exhibit reliable temperature sensitivity. Microscopic characterization reveals that the printed sensors possess uniform linewidths and well-defined outlines. After gradient sintering at 250 °C, a dense and continuous conductive path is formed, ensuring strong adhesion to the substrate. Temperature-monitoring results indicate that the sensor exhibits a nearly linear resistance response (R² > 0.999) across a broad detection range of 20–200 °C. It also demonstrates high sensitivity, characterized by a temperature coefficient of resistance (TCR) of 2.15 × 10^−3/°C at 20 °C. In repeated thermal cycling tests, the sensor demonstrates excellent repeatability and stability over 100 cycles, with resistance fluctuations kept within 0.5% and negligible hysteresis observed. These findings confirm the feasibility of using AJP technology to fabricate high-performance conformal sensors on complex surfaces, offering a promising strategy for the development of intelligent structural components in next-generation aerospace engineering. Full article

(This article belongs to the Special Issue 3D Micro/Nano Printing Technologies and Advanced Materials)

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42 pages, 3294 KB

Open AccessReview

Fusion Welding Processes Parameter Optimization for Critical Piping Systems: A Comprehensive Review

by Mohammad Sohel, Vishal S. Sharma and Aravinthan Arumugam

J. Manuf. Mater. Process. 2026, 10(1), 40; https://doi.org/10.3390/jmmp10010040 - 21 Jan 2026

Abstract

Weld quality plays a critical role in ensuring the structural integrity and long-term performance of critical piping systems used across petrochemical, oil and gas, marine, and healthcare sectors. Although gas tungsten arc welding, shielded metal arc welding, and gas metal arc welding are [...] Read more.

Weld quality plays a critical role in ensuring the structural integrity and long-term performance of critical piping systems used across petrochemical, oil and gas, marine, and healthcare sectors. Although gas tungsten arc welding, shielded metal arc welding, and gas metal arc welding are widely applied in pipe fabrication, existing studies often examine these processes independently and provide limited insight into the comparative influence of process parameters on weld morphology, microstructure, and mechanical performance. This review consolidates findings from recent research to evaluate how welding current, arc voltage, heat input, travel speed, shielding gas composition, and joint preparation interact to affect weld bead geometry, heat-affected zone evolution, tensile properties, hardness, and overall weld integrity in piping systems. The primary objective of this review is to critically compare fusion welding process parameter optimization strategies and to identify unresolved challenges in achieving controlled weld root geometry for high-integrity piping applications. Recent industrial failure investigations, particularly in ethylene oxide service piping, further underscore the importance of weld root control. Several documented leak events were traced to excessive root protrusion and inadequate interpretation of non-destructive testing data, where elevated reinforcement disrupted internal flow and promoted turbulence-induced degradation. These recurring issues highlight a broader industry challenge and strengthen the need for improved root-height optimization in critical piping applications. A significant research gap is identified in the limited optimization of weld root reinforcement, particularly in gas tungsten arc welding processes, where most reported studies document root heights exceeding 3 mm. Achieving a root height below 2 mm, which is an important requirement for reducing flow-induced turbulence and meeting industry acceptance criteria, remains insufficiently addressed. This review highlights this gap and outlines future research opportunities involving advanced parameter optimization and improved process monitoring techniques. The synthesis presented here provides a comprehensive reference for enhancing weld quality in critical piping systems and establishes a pathway for next-generation welding strategies aimed at producing high-integrity weld joints compliant with the American Society of Mechanical Engineers B31.3 requirements. Full article

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Journal of Manufacturing and Materials Processing

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