Journal of Manufacturing and Materials Processing

22 pages, 8194 KB

Open AccessArticle

Thermal and Flow Effects of Limescale on the Cooling of Slender Injection Molding Cores: A Numerical Study

by Andrea Gruber, Mayank Ambasana, Jeremy Payne, Aravind Rammohan, David O. Kazmer, Stephen P. Johnston and Davide Masato

J. Manuf. Mater. Process. 2026, 10(4), 130; https://doi.org/10.3390/jmmp10040130 - 14 Apr 2026

Abstract

Different strategies have been proposed to optimize injection mold cooling to reduce cycle time and improve efficiency. While recent research has focused on the design of additively manufactured conformal cooling inserts, the impact of mold maintenance conditions on cooling performance has received limited [...] Read more.

Different strategies have been proposed to optimize injection mold cooling to reduce cycle time and improve efficiency. While recent research has focused on the design of additively manufactured conformal cooling inserts, the impact of mold maintenance conditions on cooling performance has received limited attention, particularly regarding the formation of limescale. This work presents a numerical modeling approach to quantify the combined effects of thermal resistance and hydraulic restriction caused by limescale accumulation in high-aspect-ratio cooling channels used in slender mold cores. An integrated thermal-fluid analysis is employed to evaluate coolant flow behavior and heat-transfer performance and to assess their coupled influence on cooling efficiency and part dimensional stability. The results show that, in slender cooling channels, even thin limescale deposits can significantly reduce cooling performance, with hydraulic restriction emerging as the dominant mechanism under the investigated conditions due to the reduced effective flow area. Design strategies that reduce effective frictional length and mitigate limescale deposition reduced part temperature by approximately 10 °C and shortened cooling time by about 17%. Further optimization of coolant flow conditions yielded an additional 65% reduction in cooling time. These findings highlight the importance of integrating cooling design with preventive maintenance to achieve robust injection molding performance. Full article

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

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12 pages, 10467 KB

Open AccessArticle

Insights into GPB Zones Evolution and S’ Phase Formation in Al-Cu-Mg Alloy

by Vu Ngoc Hai, Abrar Ahmed, Tran Sy Quan, Seungwon Lee, Taiki Tsuchiya, Tetsuya Katsumi, Kazuhiko Kita and Kenji Matsuda

J. Manuf. Mater. Process. 2026, 10(4), 129; https://doi.org/10.3390/jmmp10040129 - 10 Apr 2026

Abstract

The precipitation behavior of an Al-Cu-Mg alloy during high-temperature aging was systematically examined, with particular emphasis on the evolution of Guinier–Preston–Bagaryatsky (GPB) zones and their role in the formation of the S’ phase. Aging at 250 °C led to a progressive evolution of [...] Read more.

The precipitation behavior of an Al-Cu-Mg alloy during high-temperature aging was systematically examined, with particular emphasis on the evolution of Guinier–Preston–Bagaryatsky (GPB) zones and their role in the formation of the S’ phase. Aging at 250 °C led to a progressive evolution of the precipitate state, as revealed by high-resolution transmission electron microscopy. At a short-term aging of 0.5 h, only GPB zones were observed as fine rod-like features preferentially formed on the {012} Al planes. With increased aging time, these zones gradually diminished, while S’ precipitates became predominant after approximately 2 h. Crystallographic analysis indicates that the growth direction of GPB zones is parallel to the g-(012) Al vector, corresponding to the habit plane of S’ phase. This crystallographic continuity suggests that GPB zones act as an effective precursor for S’ precipitation, contributing to not only nucleation but also to subsequent precipitate thickening. The present results provide new insight into the phase transformation pathway and precipitation mechanism governing high-temperature aging in Al-Cu-Mg alloys. Full article

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13 pages, 3211 KB

Open AccessArticle

Termite: An Open-Source Grasshopper Plugin for Parametric Slicing in Architectural Clay 3D Printing

by Julian Jauk, Lukas Gosch, Hana Vašatko and Milena Stavric

J. Manuf. Mater. Process. 2026, 10(4), 128; https://doi.org/10.3390/jmmp10040128 - 10 Apr 2026

Abstract

Over the last decade, 3D printing of clay has gained attention in architecture. Yet most slicing software is designed for thermoplastics with nozzle sizes between 0.3 and 1.0 mm. Clay printing, using larger nozzles (1–30 mm), requires precise control over path arrangement, material [...] Read more.

Over the last decade, 3D printing of clay has gained attention in architecture. Yet most slicing software is designed for thermoplastics with nozzle sizes between 0.3 and 1.0 mm. Clay printing, using larger nozzles (1–30 mm), requires precise control over path arrangement, material flow, and shrinkage—capabilities not sufficiently addressed by conventional software. This paper introduces Termite, an open-source software plugin for Rhinoceros 3D Grasshopper designed specifically for Liquid Deposition Modeling (LDM) 3D printing. The novelty of this work lies in embedding slicing logic directly into a parametric design environment, enabling explicit and flexible control of printing paths tailored to the rheological behavior of clay. The plugin supports designing, simulating, optimizing, and exporting machine data within a unified workflow. In contrast to conventional slicers, it allows variable printing parameters within a single print job, controlled inrun speeds for smoother path starts, adapted material flow at path crossings, and extrusion flattening at path ends to enhance adhesion and precision. The software was evaluated through multiple architectural-scale case studies and student-based design experiments. Results demonstrate that integrating slicing operations into parametric design workflows enables new fabrication strategies and expands accessibility of clay 3D printing for architectural applications. Full article

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28 pages, 2852 KB

Open AccessArticle

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

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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20 pages, 19535 KB

Open AccessArticle

The Effect of Structural States on the Microstructure and Mechanical Properties of Low-Activation Austenitic Steel After Long-Term Thermal Exposure at 700 °C

by Igor Litovchenko, Sergey Akkuzin, Nadezhda Polekhina, Valeria Osipova, Anna Kim, Kseniya Spiridonova and Vyacheslav Chernov

J. Manuf. Mater. Process. 2026, 10(4), 126; https://doi.org/10.3390/jmmp10040126 - 8 Apr 2026

Abstract

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads [...] Read more.

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads to the precipitation of dispersed M₂₃C₆ carbides (M = Cr, W), primarily along grain and deformation twin boundaries. After HTMT, these particles are mainly localized at grain and low-angle boundaries. With increasing aging time, both the size and volume fraction of the particles increase. In both states, the microtwin and substructure are partially retained after aging. Local regions corresponding to the early stages of recrystallization were identified after both treatments. These regions were associated with intense decomposition of the supersaturated solid solution and the coarsening of carbide particles. The mechanical properties were evaluated by tensile testing at 20, 650, and 700 °C. Aging reduced average ductility after both treatments and at all test temperatures, with this trend persisting with increasing aging time. After CR and aging, a significant scatter in elongation to failure was observed, with minimum values of ≈2–3%. This behavior is attributed to the high density of plate-like M₂₃C₆ carbides at grain and microtwin boundaries. Microcrack formation and intercrystalline fracture features were observed, directly linked to the high density of boundary carbides. These effects were less pronounced in the HTMT condition after aging. In this paper, strategies for suppressing carbide precipitation in high-manganese low-activation austenitic steels via chemical composition and thermomechanical processing optimization are discussed. Full article

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

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18 pages, 2707 KB

Open AccessArticle

Optimizing the Flexural Performance of ABS Parts Fabricated by FDM Additive Manufacturing Through a Taguchi–ANOVA Statistical Framework

by Hind B. Ali, Jamal J. Dawood, Farag M. Mohammed, Farhad M. Othman and Makram A. Fakhri

J. Manuf. Mater. Process. 2026, 10(4), 125; https://doi.org/10.3390/jmmp10040125 - 7 Apr 2026

Abstract

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), [...] Read more.

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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25 pages, 4273 KB

Open AccessArticle

CFD–Experimental Analysis of Combustion and Energy Performance in an IDR Metallurgical Furnace Fueled with a Residual Oil–Solvent Blend

by Martha Angélica Cano-Figueroa, Hugo Arcos-Gutiérrez, Raúl Pérez-Bustamante, Isaías E. Garduño, Juan R.-Moreno, José A. Betancourt-Cantera and Victor Hugo Mercado-Lemus

J. Manuf. Mater. Process. 2026, 10(4), 124; https://doi.org/10.3390/jmmp10040124 - 2 Apr 2026

Abstract

This study presents a combined computational fluid dynamics (CFD) and experimental evaluation of an adjustable direct-injection reciprocating (IDR) metallurgical furnace fueled by a multicomponent residual oil–solvent mixture. An axisymmetric CFD model, incorporating k–ω SST turbulence modeling, Eddy Dissipation Concept (EDC) combustion, and Discrete [...] Read more.

This study presents a combined computational fluid dynamics (CFD) and experimental evaluation of an adjustable direct-injection reciprocating (IDR) metallurgical furnace fueled by a multicomponent residual oil–solvent mixture. An axisymmetric CFD model, incorporating k–ω SST turbulence modeling, Eddy Dissipation Concept (EDC) combustion, and Discrete Ordinates radiation, was validated against infrared thermography and Process Analytical Technology (PAT) measurements obtained under actual operational conditions. The residual mixture operated in a turbulence-controlled regime (Da < 1), reaching maximum internal temperatures of 1199 °C and achieving a thermal efficiency of 84.6% (based on LHV). Numerical predictions agreed with thermographic data to within 5% across the stabilized operational window. Under comparable process parameters, the alternative fuel reduced cycle time and operational costs compared with diesel and natural gas whilst maintaining stable combustion. Methodological clarifications encompass a consolidated, dimensionally consistent set of equations, a QoI-based mesh-independence study, and a concise summary of the experimental configuration to enhance reproducibility. Full article

(This article belongs to the Special Issue Green Heat Transfer: Towards Sustainable Manufacturing of Advanced Thermal Technologies)

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22 pages, 6172 KB

Open AccessArticle

Data-Driven Prediction of Tensile Strength and Hardness in Ultrasonic Vibration-Assisted Friction Stir Welding of AA6082-T6

by Eman El Shrief, Omnia O. Fadel, Mohamed Baraya, Mohamed S. El-Asfoury and Ahmed Abass

J. Manuf. Mater. Process. 2026, 10(4), 123; https://doi.org/10.3390/jmmp10040123 - 31 Mar 2026

Abstract

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal [...] Read more.

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited. Full article

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29 pages, 4921 KB

Open AccessArticle

Using Machine Learning Tools in Reverse-Engineering Processes to Identify Printing Parameters in FDM-Manufactured Parts

by Brian Cruz, Álvaro Rojas, Antonio José Amell, Carlos Alberto Narváez-Tovar, Marco Antonio Velasco, Everardo Barcenas, John E. Bermeo, Yamid Gonzalo Reyes and Alejandro García-Rodríguez

J. Manuf. Mater. Process. 2026, 10(4), 122; https://doi.org/10.3390/jmmp10040122 - 31 Mar 2026

Abstract

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to ensure reliable quality assessment and support scalable reverse-engineering workflows. The objective of this study is to evaluate whether mechanical response curves obtained from tensile tests can be used to infer key [...] Read more.

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to ensure reliable quality assessment and support scalable reverse-engineering workflows. The objective of this study is to evaluate whether mechanical response curves obtained from tensile tests can be used to infer key manufacturing parameters, specifically part orientation, layer thickness, and infill density. Force–displacement and stress–strain data were transformed into image-based representations and classified using several individual and ensemble machine learning models. In addition, the influence of applying a moving-average filter to smooth the curve-derived images was analyzed. Ensemble methods, particularly the AdaBoost classifier, achieved the best performance across the evaluated variables, with the highest accuracy obtained from unfiltered stress–strain images. Under limited-data conditions, ensemble models consistently outperformed individual classifiers, whereas Multilayer Perceptron and Support Vector Machine models exhibited more stable but lower predictive accuracy. These results demonstrate that mechanical response curves contain relevant information about manufacturing conditions and can be used to infer FDM printing parameters. The proposed approach offers a potential non-destructive framework for parameter identification in additively manufactured components, thereby improving traceability and quality control in additive manufacturing processes. Full article

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45 pages, 6271 KB

Open AccessReview

Progresses and Challenges in Additive Manufacturing of Bulk Metallic Glasses

by Md Mahbubur Rahman, Raju Ahammad, Asif Karim Neon, Mukitur Rhaman, Md Jonaet Ansari, Md Nizam Uddin, Md Mainul Islam and Muhammad Altaf Nazir

J. Manuf. Mater. Process. 2026, 10(4), 121; https://doi.org/10.3390/jmmp10040121 - 30 Mar 2026

Abstract

Bulk metallic glasses (BMGs) are a type of amorphous metal with a high degree of mechanical strength, elasticity and corrosion resistance, properties that are highly influenced by composition and the processing of the material. BMGs can be applied in advanced engineering fields, such [...] Read more.

Bulk metallic glasses (BMGs) are a type of amorphous metal with a high degree of mechanical strength, elasticity and corrosion resistance, properties that are highly influenced by composition and the processing of the material. BMGs can be applied in advanced engineering fields, such as aerospace, biomedical, MEMS, and industrial applications. Additive manufacturing (AM) is revolutionary in producing intricate BMG parts whilst maintaining the amorphous structure. The current review critically evaluates the recent development in AM of BMGs, such as the development of selective laser melting, electron beam melting, and directed energy deposition, and new classes of hybrid strategies. Enhancements in dimensional accuracy, amorphous retention, microstructural tailoring and functional performance are emphasized along with computational and real-time process optimization strategies to improve overall manufacturing efficiency and material quality. Subsequently, the challenges that still exist are addressed in the review, including crystallization during printing, the buildup of stress, printable thickness, complicated geometries, oxidation, contamination, and heterogeneous amorphous fractions. Lastly, multi-material printing, scalable AM approaches, and AI-assisted design solutions are key features of future perspectives to solve existing restrictions. The review provides an excellent guidance for the researcher and engineer interested in advancing additive manufacturing of BMGs with the best structure–property relations. Full article

(This article belongs to the Special Issue Advances in Additive Manufacturing of Metal Alloys: Microstructure, Mechanical Behavior, and Surface Performance)

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38 pages, 5611 KB

Open AccessReview

Comprehensive Study and Analysis of Tapping and Nut Bolt Joints Used in Subsea Applications

by Vipul Mehta, Jitendra Yadav, Varun Pratap Singh, Tabrej Khan and Tamer A. Sebaey

J. Manuf. Mater. Process. 2026, 10(4), 120; https://doi.org/10.3390/jmmp10040120 - 30 Mar 2026

Abstract

Threaded fasteners and tapping joints are essential for the structural integrity and leak-proof performance of subsea systems subjected to high external pressure, aggressive corrosion, and complex cyclic loading. This study presents a comprehensive, systematically structured review of experimental, analytical, and numerical investigations of [...] Read more.

Threaded fasteners and tapping joints are essential for the structural integrity and leak-proof performance of subsea systems subjected to high external pressure, aggressive corrosion, and complex cyclic loading. This study presents a comprehensive, systematically structured review of experimental, analytical, and numerical investigations of nut–bolt and threaded connections used in deep- and ultra-deepwater applications. The literature is classified based on governing performance parameters, including thread engagement mechanics, preload retention, fracture behavior, corrosion–fatigue interaction, material evolution, and environmental effects such as hydrostatic pressure and thermal gradients. Experimental observations are critically synthesized with finite element modeling to interpret stress distributions, failure mode transitions, and sealing reliability. A comparative material selection framework is developed by linking conventional carbon steels with advanced alloys such as duplex stainless steels, titanium, and nickel-based materials for long-term subsea service. The novelty of this review lies in the development of an integrated, design-oriented framework that unifies engagement optimization, preload control, fracture modeling strategies, material selection, and environmental coupling into a single engineering interpretation for subsea fastening systems, which has not been collectively addressed in previous studies. The presented synthesis provides direct application guidelines for improving the design, analysis, and operational reliability of subsea bolted joints. Full article

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37 pages, 11887 KB

Open AccessArticle

Additive Manufacturing of High Heels Using the Input–Transformation–Output Model: Comparative Evaluation of PLA, ABS and ABS Photopolymer Resin Materials

by María Alejandra García Rojas, Kevin Santiago Hernández Urbina, Sylvia María Villarreal-Archila, Jairo Núñez Rodríguez and Ángel Ortiz Bas

J. Manuf. Mater. Process. 2026, 10(4), 119; https://doi.org/10.3390/jmmp10040119 - 30 Mar 2026

Abstract

The use of additive manufacturing in structural applications has increased in industry; however, reliable material selection criteria remain limited when printed components must withstand real service loads. The following study provides a comprehensive evaluation of polymeric materials (PLA filament, ABS filament, and ABS-like [...] Read more.

The use of additive manufacturing in structural applications has increased in industry; however, reliable material selection criteria remain limited when printed components must withstand real service loads. The following study provides a comprehensive evaluation of polymeric materials (PLA filament, ABS filament, and ABS-like resin) used in additive manufacturing technologies for the production of footwear heels. Consequently, five heel models were designed using reverse engineering based on real industry references and analyzed within a decision framework based on the Input–Transformation–Output (ITO) model. Within this framework, each material was subjected to static mechanical tests (tensile, compression, flexural and hardness), scanning electron microscopy (SEM) analysis and numerical simulations. In addition, functional tests were carried out by mounting the printed heels on real sandals, allowing for evaluation of their performance under service conditions. Significant differences in surface morphology were observed, attributable to the physical state and consolidation mechanism of each material. Uncontrolled environmental conditions during printing and testing were identified as a limitation affecting reproducibility. The ABS-like resin showed the highest compressive load capacity (10.8 kN), together with a tensile strength of 14.99 MPa and a deformation at break of 0.076 mm/mm. SEM analysis revealed a more homogeneous surface morphology and greater structural continuity after curing, consistent with the numerical simulations, which predicted stresses between 19.98 and 196.23 MPa, displacements up to 8.917 mm and unit strains up to 0.1378. The integrated interpretation of the experimental, microstructural and functional results provides technical criteria for material selection in reverse-engineered footwear components and structural elements manufactured by additive manufacturing. Full article

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

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20 pages, 2182 KB

Open AccessArticle

Physics-Aligned Data Augmentation for Reliable Property Prediction in Direct Ink Writing Under Extreme Data Scarcity

by Biva Gyawali, Pavan Akula, Kamran Alba and Vahid Nasir

J. Manuf. Mater. Process. 2026, 10(4), 118; https://doi.org/10.3390/jmmp10040118 - 30 Mar 2026

Abstract

Reliable property prediction in extrusion-based additive manufacturing remains challenging under extreme data scarcity (e.g., sample size of <50), particularly when experiments are constrained by designed studies such as Taguchi orthogonal arrays. In direct ink writing of lignocellulosic composites, limited experimental runs restrict the [...] Read more.

Reliable property prediction in extrusion-based additive manufacturing remains challenging under extreme data scarcity (e.g., sample size of <50), particularly when experiments are constrained by designed studies such as Taguchi orthogonal arrays. In direct ink writing of lignocellulosic composites, limited experimental runs restrict the development of predictive models capable of guiding formulation and process optimization. This study introduces a physics-consistent data augmentation framework to enhance predictive reliability while preserving material-consistent behavior. Synthetic data are evaluated using four criteria: sensitivity to augmentation size, distributional consistency with experimental observations, stability with respect to boosting depth in regression modeling, and preservation of physics-consistent factor hierarchies through interpretability analysis. The framework is validated using compressive strength data from direct ink writing experiments conducted under an extremely small data regime. Results show that augmentation performance depends on the augmentation scale and model capacity. Variational autoencoder-based augmentation produced more stable and physically consistent predictions than conditional tabular generative adversarial networks in this application. Increasing predictive accuracy alone, or applying excessive augmentation, can distort material hierarchies and reduce physics consistency. The proposed evaluation framework supports reliable and interpretable property prediction in additive manufacturing when experimental data are severely limited. Full article

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

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35 pages, 7857 KB

Open AccessArticle

Toward Large Language Model-Driven Symbolic Topology Optimisation for Rapid Structural Concept Generation in Manufacturable Design

by Musaddiq Al Ali

J. Manuf. Mater. Process. 2026, 10(4), 117; https://doi.org/10.3390/jmmp10040117 - 30 Mar 2026

Abstract

Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces [...] Read more.

Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces a generative design framework, referred to as Large Language Model-Driven Symbolic Topology Optimisation (LLM-DSTO), in which large language models act as conceptual design generators. Engineering problems are formulated through structured textual descriptions defining the design domain, boundary conditions, loading scenarios, and material constraints. The language model interprets these inputs and produces symbolic representations of candidate structural topologies. The generated layouts are evaluated using physics-informed objective functions and refined iteratively through lightweight computational procedures. The resulting designs exhibit coherent load paths, strong structural connectivity, and material distributions that are consistent with practical manufacturing requirements, including additive manufacturing constraints. The proposed framework is validated across structural, thermal, thermofluid, and compliant mechanism design problems. Quantitative results show that the generated structures achieve approximately 87.5% of the stiffness obtained using the classical SIMP method for the cantilever benchmark, while reaching about 94.3% of the thermal performance in heat sink optimisation. These results are obtained without repeated finite element simulations, demonstrating a significant reduction in computational cost. In addition, the framework is extended to three-dimensional topology generation, producing volumetric structures under a 50% material volume constraint with coherent internal load paths. Full article

(This article belongs to the Special Issue Technological Advances and Industrial Applications in Intelligent Manufacturing)

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32 pages, 9104 KB

Open AccessArticle

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

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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23 pages, 5229 KB

Open AccessArticle

Experimental Investigation of Surface Integrity Analysis Using Machine Learning for Nano-Powder Mixed Electrical Discharge Machining

by Amreeta R. Kaigude, Nitin K. Khedkar and Vijaykumar S. Jatti

J. Manuf. Mater. Process. 2026, 10(4), 115; https://doi.org/10.3390/jmmp10040115 - 28 Mar 2026

Abstract

This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO₂) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response [...] Read more.

This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO₂) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response Surface Methodology (RSM) to systematically analyze the machining of AISI D2 tool steel using copper electrodes. The study examined five critical process parameters, gap current (Ip), pulse-on duration (Ton), pulse-off time (Toff), gap voltage (V), and powder concentration, evaluating their combined effects on surface roughness (SR), surface crack density (SCD), and residual stress characteristics. Advanced characterization techniques including scanning electron microscopy (SEM) were employed to analyze surface topography and subsurface microstructural changes. The optimization process successfully identified optimal machining conditions of current = 9 A, Ton = 100 µs, Toff = 10 µs, and gap voltage = 65 V, achieving exceptional surface quality with a minimum surface roughness of 3.22 µm. Remarkably, these optimized parameters resulted in crack-free surfaces with zero surface crack density and minimal residual stress values across the 2θ range of 90° to 180°. To enhance predictive capabilities, supervised machine learning algorithms were implemented to model surface roughness behavior. Comparative analysis of classification algorithms demonstrated that Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), and Gaussian Naïve Bayes achieved superior performance with F1-scores of 0.88 and prediction accuracies of 90%. The integration of sustainable Jatropha biodielectric with TiO₂ nanoparticles represents a significant advancement in environmentally conscious precision machining, while the machine learning approach establishes a robust framework for intelligent process optimization and quality prediction in advanced manufacturing applications. Full article

(This article belongs to the Topic Advanced Manufacturing and Surface Technology, 2nd Edition)

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17 pages, 3566 KB

Open AccessArticle

Integrated Optimization for Reducing Injection Molding Defects in Charcoal Canisters

by Mohsen Hedayati-Dezfooli and Mehdi Moayyedian

J. Manuf. Mater. Process. 2026, 10(4), 114; https://doi.org/10.3390/jmmp10040114 - 27 Mar 2026

Abstract

This study presents an integrated optimization framework that combines the Design of Experiments (DOE) approach with Machine Learning (ML) techniques to minimize defects in the injection molding of Fuel Vapor Charcoal Canisters. The research focuses on five critical process parameters—melt temperature, mold temperature, [...] Read more.

This study presents an integrated optimization framework that combines the Design of Experiments (DOE) approach with Machine Learning (ML) techniques to minimize defects in the injection molding of Fuel Vapor Charcoal Canisters. The research focuses on five critical process parameters—melt temperature, mold temperature, filling time, pressure holding time, and pure cooling time—whose combined influence on major molding defects (warpage, shrinkage, shear stress, residual stress, and short shots) was systematically investigated. A Taguchi L25 orthogonal array was employed to structure the experiments and identify the optimal parameter levels through signal-to-noise (S/N) ratio analysis using the “smaller-the-better” quality criterion. The Taguchi results revealed that pressure holding time was the most influential factor, followed by mold temperature and melt temperature. Simulation results from SolidWorks Plastics confirmed the reduction in major defects under the optimized settings. To further validate and generalize the DOE findings, a Random Forest regression model was trained on the same dataset to capture nonlinear interactions between parameters. The model achieved an average RMSE of 2.451 ± 0.591 in five-fold cross-validation, demonstrating strong predictive accuracy. Feature importance analysis indicated that pressure holding time accounted for approximately 77.5% of the variance in the defect index, reaffirming its dominant role. A 3D response surface of the global parameter space (mold temperature vs. pressure holding time) revealed a distinct minimum defect region, consistent with the DOE-optimized settings. The Taguchi analysis identified the optimal parameter settings as Melt Temperature at Level 2, Mould Temperature at Level 3, Filling Time at Level 4, Pressure Holding Time at Level 5, and Pure Cooling Time at Level 4, which collectively produced the highest S/N ratios and the lowest overall defect index. The overall discrepancy between DOE and ML predictions was only 12.5%, confirming methodological consistency. The integration of DOE and ML not only enhances parameter interpretability and defect prediction accuracy but also provides a scalable, data-driven approach for intelligent process control and quality assurance in automotive injection molding. Full article

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

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19 pages, 3679 KB

Open AccessArticle

Guide to a Deterministic Control of Laser Materials Processing with Dynamic Beam Shaping

by Rudolf Weber, Thomas Graf, Kim Glumann, Christian Hagenlocher, Ami Spira, Nina Armon, Ehud Greenberg, Rachel Assa and Eyal Shekel

J. Manuf. Mater. Process. 2026, 10(4), 113; https://doi.org/10.3390/jmmp10040113 - 27 Mar 2026

Abstract

Dynamic beam shaping opens new possibilities for improving the quality and productivity of industrial laser material processing applications such as welding and cutting. However, dynamic beam shaping involves time constants and frequencies that must be selected correctly to successfully modify a given laser [...] Read more.

Dynamic beam shaping opens new possibilities for improving the quality and productivity of industrial laser material processing applications such as welding and cutting. However, dynamic beam shaping involves time constants and frequencies that must be selected correctly to successfully modify a given laser process. This paper proposes a standardized nomenclature for the possible types of dynamic beam shaping and the resulting dynamic process modifications, and relates these to characteristic time constants and frequencies at which the process modifications have a particularly strong influence on the process. These characteristic frequencies define three process regimes that have distinctly different effects on the process. An overview of typical time constants and frequencies in laser processes aids in understanding the occurrence of characteristic frequencies. Knowledge of the process regimes allows for a systematic selection of frequencies in dynamic beam shaping to achieve targeted dynamic process modifications, e.g., for pore reduction. Using a laser system capable of dynamic beam shaping at frequencies of up to 80 MHz, the influence of the three process zones on the porosity of the weld was demonstrated using deep welds in cast aluminum as an example. Full article

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55 pages, 3716 KB

Open AccessReview

Digital Enablers of the Circular Economy: A Systematic Review of Applications, Barriers, and Future Directions

by Parinaz Pourrahimian, Saleh Seyedzadeh, Behrouz Arabi, Daniel Kahani and Saeid Lotfian

J. Manuf. Mater. Process. 2026, 10(4), 112; https://doi.org/10.3390/jmmp10040112 - 25 Mar 2026

Abstract

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications [...] Read more.

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

(This article belongs to the Special Issue AI-Driven Smart Manufacturing: Bridging Data Innovation, Industrial Practice, and Ethical Intelligence)

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