Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.004
Laine Mears
{"title":"A Welcome from the Editor-in-Chief","authors":"Laine Mears","doi":"10.1016/j.mfglet.2025.06.004","DOIUrl":"10.1016/j.mfglet.2025.06.004","url":null,"abstract":"","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Page 8"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.098
Marc Corfmat, Charles Ringham, Masakazu Soshi
Additive manufacturing (AM) processes, such as Fused Filament Fabrication (FFF) and Directed Energy Deposition (DED), are highly susceptible to heat accumulation and uneven cooling, leading to residual stresses, geometric inaccuracies, and compromised material properties. While the magnitude of these effects is far smaller in FFF, effective thermal management is essential to address these challenges in DED. This paper proposes a novel adaptive toolpath control strategy that dynamically adjusts the deposition path of the subsequent layer based on the thermal gradient of the previous layer. While DED is the primary focus for this implementation, initial experimentation leveraged FFF due to its cost-effectiveness and similar thermal characteristics to DED, allowing for efficient testing and validation of the proposed strategy. Four infill stacking patterns—SAME, FLIP, ROTATE SAME, and ROTATE FLIP—were tested, revealing that FLIP and ROTATE FLIP produced more symmetric thermal distributions. These results demonstrate the feasibility of adaptive toolpath strategies for improving thermal management in DED, with future work focused on advanced algorithms, thermal simulations, and validation in DED applications.
{"title":"Adaptive toolpath for improved thermal management in additive manufacturing (AM)","authors":"Marc Corfmat, Charles Ringham, Masakazu Soshi","doi":"10.1016/j.mfglet.2025.06.098","DOIUrl":"10.1016/j.mfglet.2025.06.098","url":null,"abstract":"<div><div>Additive manufacturing (AM) processes, such as Fused Filament Fabrication (FFF) and Directed Energy Deposition (DED), are highly susceptible to heat accumulation and uneven cooling, leading to residual stresses, geometric inaccuracies, and compromised material properties. While the magnitude of these effects is far smaller in FFF, effective thermal management is essential to address these challenges in DED. This paper proposes a novel adaptive toolpath control strategy that dynamically adjusts the deposition path of the subsequent layer based on the thermal gradient of the previous layer. While DED is the primary focus for this implementation, initial experimentation leveraged FFF due to its cost-effectiveness and similar thermal characteristics to DED, allowing for efficient testing and validation of the proposed strategy. Four infill stacking patterns—SAME, FLIP, ROTATE SAME, and ROTATE FLIP—were tested, revealing that FLIP and ROTATE FLIP produced more symmetric thermal distributions. These results demonstrate the feasibility of adaptive toolpath strategies for improving thermal management in DED, with future work focused on advanced algorithms, thermal simulations, and validation in DED applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 832-838"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.009
Vipul Bansal , Shiyu Zhou , Nicolas Strike
Physics-Informed Neural Networks (PINNs) are a popular scientific machine learning framework used to solve partial differential equations (PDEs). One of the common applications of PINNs is in solving fluid flow problems using the Navier–Stokes (NS) equations. The NS equations are a set of PDEs that describe the flow of a viscous fluid and have been extensively applied in manufacturing problems, such as modeling flow in injection molding or the flow of molten metal in additive manufacturing. Solving a single PINN with various boundary conditions requires training a unified model to predict the flow field for each specific boundary condition setup. This poses a challenge in training PINNs due to the limited number of samples that can be taken from the parametric space corresponding to various boundary conditions, often leading to poor-quality solutions. To address this, we propose a two-step solution to solve PINNs for the Navier–Stokes equations with various boundary conditions. The proposed method enables PINNs to learn effectively both from the domain and from parametric spaces. This two-step approach provides the model with a finer initial understanding of the domain space and then shifts to sampling from the parametric space to enhance its knowledge of the parametric variations. Numerical studies demonstrate the effectiveness of the proposed approach compared to direct training of PINNs. Increased knowledge about domain space provides the model with better learning of boundary conditions and lower PDE residuals. The proposed method uses the same computational requirements as direct training but provides better convergence. Furthermore, the ability to learn parametric boundary conditions enables PINNs to be applied to a variety of versatile applications.
{"title":"Two step training a single physics-informed neural network for solving Navier Stokes equations with various boundary conditions","authors":"Vipul Bansal , Shiyu Zhou , Nicolas Strike","doi":"10.1016/j.mfglet.2025.06.009","DOIUrl":"10.1016/j.mfglet.2025.06.009","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) are a popular scientific machine learning framework used to solve partial differential equations (PDEs). One of the common applications of PINNs is in solving fluid flow problems using the Navier–Stokes (NS) equations. The NS equations are a set of PDEs that describe the flow of a viscous fluid and have been extensively applied in manufacturing problems, such as modeling flow in injection molding or the flow of molten metal in additive manufacturing. Solving a single PINN with various boundary conditions requires training a unified model to predict the flow field for each specific boundary condition setup. This poses a challenge in training PINNs due to the limited number of samples that can be taken from the parametric space corresponding to various boundary conditions, often leading to poor-quality solutions. To address this, we propose a two-step solution to solve PINNs for the Navier–Stokes equations with various boundary conditions. The proposed method enables PINNs to learn effectively both from the domain and from parametric spaces. This two-step approach provides the model with a finer initial understanding of the domain space and then shifts to sampling from the parametric space to enhance its knowledge of the parametric variations. Numerical studies demonstrate the effectiveness of the proposed approach compared to direct training of PINNs. Increased knowledge about domain space provides the model with better learning of boundary conditions and lower PDE residuals. The proposed method uses the same computational requirements as direct training but provides better convergence. Furthermore, the ability to learn parametric boundary conditions enables PINNs to be applied to a variety of versatile applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 48-58"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.061
Walid Al Asad , Shubha Majumder , Karuna Nambi Gowri , Martin W. King , Xin Zhao
This study explores the fabrication of barbed sutures of biodegradable polymers, such as P4HB and Cagut, using a femtosecond laser. Barbed sutures are in high demand for minimally invasive procedures, with the benefits of reducing the need for knots, enhancing wound closure stability and minimizing tissue trauma. Traditional approaches, such as mechanical cutting and longer-pulses lasers, result in imprecise cutting and extended thermal damage. In contrast, ultrashort pulse durations of femtosecond lasers enable high-precision cutting with the added benefits of minimal heat-affected zones. This research investigates the effects of key laser parameters, such as laser fluence, repetition rate, overlapping ratio and number of scans, on barb quality and identifies the optimal conditions for consistent, high-quality barbs with sharp tips and minimal thermal damage. Moreover, the threshold fluence values established here, for P4HB and Catgut, serve as a reference for future study. Results demonstrate that femtosecond laser technology can be a promising alternative to traditional barb fabrication techniques.
{"title":"Femtosecond laser micromachining of barbed sutures","authors":"Walid Al Asad , Shubha Majumder , Karuna Nambi Gowri , Martin W. King , Xin Zhao","doi":"10.1016/j.mfglet.2025.06.061","DOIUrl":"10.1016/j.mfglet.2025.06.061","url":null,"abstract":"<div><div>This study explores the fabrication of barbed sutures of biodegradable polymers, such as P4HB and Cagut, using a femtosecond laser. Barbed sutures are in high demand for minimally invasive procedures, with the benefits of reducing the need for knots, enhancing wound closure stability and minimizing tissue trauma. Traditional approaches, such as mechanical cutting and longer-pulses lasers, result in imprecise cutting and extended thermal damage. In contrast, ultrashort pulse durations of femtosecond lasers enable high-precision cutting with the added benefits of minimal heat-affected zones. This research investigates the effects of key laser parameters, such as laser fluence, repetition rate, overlapping ratio and number of scans, on barb quality and identifies the optimal conditions for consistent, high-quality barbs with sharp tips and minimal thermal damage. Moreover, the threshold fluence values established here, for P4HB and Catgut, serve as a reference for future study. Results demonstrate that femtosecond laser technology can be a promising alternative to traditional barb fabrication techniques.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 517-523"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.036
O. Olaogun , P.A. Olubambi
The adoption of hybrid welding in manufacturing sectors that produce heavy-duty machinery is increasing. Manufacturing industries that produce heavy duty machinery are increasingly utilizing hybrid welding. This is as a result of several drawbacks of standalone welding processes, such as undercut formation, spatter formation and low weld metal toughness. TIG-MIG hybrid welding, a special, low-cost hybrid welding process incorporating the properties of both TIG and MIG welding processes, produces precise welds. While this hybrid technique combines the benefits and improvement in its quality, its efficiency can be enhanced. Therefore, the post weld heat treatment of the TIG-MIG hybrid welded joint is proposed. This research presents an investigation of post weld heat treatment on TIG-MIG hybrid welded AISI 1007 steel. The hybrid welding procedure was carried out on a 7 mm AISI 1007 steel plate. The butt joint configuration had a single V-notch groove. The hybridized TIG-MIG welded joint is subjected to Post-Weld Heat Treatment (PWHT) in both normalizing and annealing conditions at 850 °C. Tensile, microhardness and charpy impact test were employed to investigate the mechanical properties of the hybrid welded joint. The microstructural examination was achieved using Raman and SEM with EDS attachment. Findings show that post weld heat treatments, particularly normalizing and annealing, improve the uniformity and refinement of the grain structure in the as-weld TIG-MIG hybrid welded joints. However, unlike in the normalized condition, microstructural images of the annealed TIG-MIG interface confirm the presence of carbide precipitates. The as-welded condition exhibits higher strength, while heat-treated conditions enhance ductility and toughness. Selecting the optimal welding condition should depend on the balance of strength, ductility, and toughness required for the application.
在生产重型机械的制造部门中，混合焊接的采用正在增加。生产重型机械的制造业越来越多地使用混合焊接。这是由于独立焊接工艺的几个缺点造成的，如凹边形成、飞溅形成和焊接金属韧性低。TIG-MIG混合焊接是一种特殊的低成本混合焊接工艺，结合了TIG和MIG焊接工艺的特性，可以产生精确的焊缝。虽然这种混合技术结合了其质量的优点和改进，但其效率可以提高。为此，提出了TIG-MIG复合焊接接头的焊后热处理方法。研究了TIG-MIG复合焊接AISI 1007钢的焊后热处理工艺。对7 mm AISI 1007钢板进行了复合焊接。对接配置有一个单一的v形缺口槽。混合TIG-MIG焊接接头在850 °C正火和退火条件下进行焊后热处理（PWHT）。采用拉伸试验、显微硬度试验和夏比冲击试验对复合焊接接头的力学性能进行了研究。利用拉曼光谱和扫描电子显微镜（SEM）进行了显微组织分析。结果表明，焊后热处理，特别是正火和退火，改善了TIG-MIG复合焊接接头的均匀性和晶粒组织的细化。然而，与归一化条件不同，退火TIG-MIG界面的显微组织图像证实了碳化物沉淀的存在。焊接状态下的合金具有较高的强度，而热处理状态下的合金具有较高的塑性和韧性。选择最佳焊接条件应取决于应用所需的强度，延展性和韧性的平衡。
{"title":"An investigation of post-weld heat treatment for welded AISI 1007 steel using TIG-MIG hybrid welding technique","authors":"O. Olaogun , P.A. Olubambi","doi":"10.1016/j.mfglet.2025.06.036","DOIUrl":"10.1016/j.mfglet.2025.06.036","url":null,"abstract":"<div><div>The adoption of hybrid welding in manufacturing sectors that produce heavy-duty machinery is increasing. Manufacturing industries that produce heavy duty machinery are increasingly utilizing hybrid welding. This is as a result of several drawbacks of standalone welding processes, such as undercut formation, spatter formation and low weld metal toughness. TIG-MIG hybrid welding, a special, low-cost hybrid welding process incorporating the properties of both TIG and MIG welding processes, produces precise welds. While this hybrid technique combines the benefits and improvement in its quality, its efficiency can be enhanced. Therefore, the post weld heat treatment of the TIG-MIG hybrid welded joint is proposed. This research presents an investigation of post weld heat treatment on TIG-MIG hybrid welded AISI 1007 steel. The hybrid welding procedure was carried out on a 7 mm AISI 1007 steel plate. The butt joint configuration had a single V-notch groove. The hybridized TIG-MIG welded joint is subjected to Post-Weld Heat Treatment (PWHT) in both normalizing and annealing conditions at 850 °C. Tensile, microhardness and charpy impact test were employed to investigate the mechanical properties of the hybrid welded joint. The microstructural examination was achieved using Raman and SEM with EDS attachment. Findings show that post weld heat treatments, particularly normalizing and annealing, improve the uniformity and refinement of the grain structure in the as-weld TIG-MIG hybrid welded joints. However, unlike in the normalized condition, microstructural images of the annealed TIG-MIG interface confirm the presence of carbide precipitates. The as-welded condition exhibits higher strength, while heat-treated conditions enhance ductility and toughness. Selecting the optimal welding condition should depend on the balance of strength, ductility, and toughness required for the application.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 294-305"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.007
Derrick Mirindi , David Sinkhonde , Frederic Mirindi
Plastic composites provide an eco-friendly substitute for conventional construction materials. Indeed, recycling waste plastic represents a progressive approach to waste management with the aim of mitigating the growing issue of pollution in urban environments. Our research aims to review the physical properties, including water absorption (WA) and thickness swelling (TS), and mechanical properties, such as the internal bond (IB), the modulus of rupture (MOR), and the modulus of elasticity (MOE), of the latest findings made of wood panels combined with plastic. We are focusing on three types of plastic, namely polyethylene terephthalate (PET), polypropylene (PP), and high-density polyethylene (HDPE). In addition, we employed machine learning (ML) algorithms, including the hierarchical clustering dendrogram, the Pearson correlation coefficient, the support vector regression, the random forest (RF), and the decision tree (DT) for prediction analysis. For instance, the results indicate that combining HDPE with wood pulp fiber increases the MOR (42.45 MPa) and MOE (66.7 MPa), respectively. Furthermore, mixed plastics such as PET, HDPE, PP, and LDPE improve the dimensional stability by reducing the WA (0.32 %) and TS (0.18 %), respectively. In most cases, these results meet the minimum standard requirement for general-purpose boards, according with the American National Standard for Particleboard (ANSI/A208.1-1999), the European standard (EN 312), and Brazilian Association of Technical (ABNT NBR) standard. In addition, the dendrogram identifies three primary clusters with varying Euclidean distances, indicating the performance of wood-plastic panels for both physical and mechanical properties. Notably, the dimensional stability among panels is stronger than that of mechanical properties. The correlation matrix is important for selecting an appropriate plastic. The SVR, RF, and DT algorithms make predictions by analyzing the properties of the panel. For instance, the DT algorithm shows that when WA is less than 25 %, the predicted value of TS is 0.24 %; in addition, when the value is between 25 % and 75 %, TS is equal to 7.92 %; also, when WA is greater than 75 %, TS is predicted to be at 13.7 %. This innovative method of utilizing ML and DL for prediction opens new possibilities for the use of plastic in panel production, as it allows for the selection of suitable materials and fabrication techniques to create a wood-plastic composite.
{"title":"Application of machine learning to predict the properties of wood- composite made from PET, HDPE, and PP fibres","authors":"Derrick Mirindi , David Sinkhonde , Frederic Mirindi","doi":"10.1016/j.mfglet.2025.06.007","DOIUrl":"10.1016/j.mfglet.2025.06.007","url":null,"abstract":"<div><div>Plastic composites provide an eco-friendly substitute for conventional construction materials. Indeed, recycling waste plastic represents a progressive approach to waste management with the aim of mitigating the growing issue of pollution in urban environments. Our research aims to review the physical properties, including water absorption (WA) and thickness swelling (TS), and mechanical properties, such as the internal bond (IB), the modulus of rupture (MOR), and the modulus of elasticity (MOE), of the latest findings made of wood panels combined with plastic. We are focusing on three types of plastic, namely polyethylene terephthalate (PET), polypropylene (PP), and high-density polyethylene (HDPE). In addition, we employed machine learning (ML) algorithms, including the hierarchical clustering dendrogram, the Pearson correlation coefficient, the support vector regression, the random forest (RF), and the decision tree (DT) for prediction analysis. For instance, the results indicate that combining HDPE with wood pulp fiber increases the MOR (42.45 MPa) and MOE (66.7 MPa), respectively. Furthermore, mixed plastics such as PET, HDPE, PP, and LDPE improve the dimensional stability by reducing the WA (0.32 %) and TS (0.18 %), respectively. In most cases, these results meet the minimum standard requirement for general-purpose boards, according with the American National Standard for Particleboard (ANSI/A208.1-1999), the European standard (EN 312), and Brazilian Association of Technical (ABNT NBR) standard. In addition, the dendrogram identifies three primary clusters with varying Euclidean distances, indicating the performance of wood-plastic panels for both physical and mechanical properties. Notably, the dimensional stability among panels is stronger than that of mechanical properties. The correlation matrix is important for selecting an appropriate plastic. The SVR, RF, and DT algorithms make predictions by analyzing the properties of the panel. For instance, the DT algorithm shows that when WA is less than 25 %, the predicted value of TS is 0.24 %; in addition, when the value is between 25 % and 75 %, TS is equal to 7.92 %; also, when WA is greater than 75 %, TS is predicted to be at 13.7 %. This innovative method of utilizing ML and DL for prediction opens new possibilities for the use of plastic in panel production, as it allows for the selection of suitable materials and fabrication techniques to create a wood-plastic composite.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 24-35"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.008
Jeongmo Kang, Sungchul Jee
This study presents an advanced cross-coupling control (CCC) system for five-axis machine tools that enhances contour accuracy during simultaneous machining. The method considers both the dynamic constraints of feed drive and the intricate kinematic relationships between the workpiece coordinate system (WCS) and the machine coordinate system (MCS). The method ensures precise control of tool trajectories and orientations by calculating compensation vectors for both the translational and rotational axes. These dynamically respect the constraints of each feed drive when minimizing contour and orientation errors. In contrast to recent works that sought to improve contour accuracy, our approach reduces any need for complex mathematical modeling, facilitating immediate integration with various computerized numerical control (CNC) machine tool configurations. Experimentally, machining contour accuracy and surface quality improved; the method is very precise. Again, the method can be seamlessly integrated with existing CNC machine tools; this ensures immediate industrial applications.
{"title":"A novel five-axis cross-coupling control system that considers the motion and dynamic constraints of feed drive systems","authors":"Jeongmo Kang, Sungchul Jee","doi":"10.1016/j.mfglet.2025.06.008","DOIUrl":"10.1016/j.mfglet.2025.06.008","url":null,"abstract":"<div><div>This study presents an advanced cross-coupling control (CCC) system for five-axis machine tools that enhances contour accuracy during simultaneous machining. The method considers both the dynamic constraints of feed drive and the intricate kinematic relationships between the workpiece coordinate system (WCS) and the machine coordinate system (MCS). The method ensures precise control of tool trajectories and orientations by calculating compensation vectors for both the translational and rotational axes. These dynamically respect the constraints of each feed drive when minimizing contour and orientation errors. In contrast to recent works that sought to improve contour accuracy, our approach reduces any need for complex mathematical modeling, facilitating immediate integration with various computerized numerical control (CNC) machine tool configurations. Experimentally, machining contour accuracy and surface quality improved; the method is very precise. Again, the method can be seamlessly integrated with existing CNC machine tools; this ensures immediate industrial applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 36-47"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.062
Fengfeng Zhou , Xingyu Fu , Nobin Myeong , Siying Chen , Martin Byung-Guk Jun
This paper introduces a cost-effective laser direct writing method for fabricating flexible electrodes. Micron-sized copper powder is combined with polypropylene (pp-Cu) and Loctite® Extreme Glue (glue-Cu) to create a metal-polymer composite feedstock. A low-power carbon dioxide laser is used to process the feedstock to build conductive pathways along the laser ablation toolpath. The laser-processed electrode made from the pp-Cu composite exhibits a resistance of approximately 20MΩ, while the glue-Cu electrode demonstrates a resistance of around 2kΩ. Further, the bent electrode retains its conductivity at a bending radius of 30 mm, demonstrating its potential for use in flexible sensor applications. This approach enables the fabrication of flexible and conformal electronics without requiring protective gases, using a cost-efficient laser system.
{"title":"Cost-efficient laser direct writing of flexible electrodes using metal matrix composites","authors":"Fengfeng Zhou , Xingyu Fu , Nobin Myeong , Siying Chen , Martin Byung-Guk Jun","doi":"10.1016/j.mfglet.2025.06.062","DOIUrl":"10.1016/j.mfglet.2025.06.062","url":null,"abstract":"<div><div>This paper introduces a cost-effective laser direct writing method for fabricating flexible electrodes. Micron-sized copper powder is combined with polypropylene (pp-Cu) and Loctite® Extreme Glue (glue-Cu) to create a metal-polymer composite feedstock. A low-power carbon dioxide laser is used to process the feedstock to build conductive pathways along the laser ablation toolpath. The laser-processed electrode made from the pp-Cu composite exhibits a resistance of approximately 20MΩ, while the glue-Cu electrode demonstrates a resistance of around 2kΩ. Further, the bent electrode retains its conductivity at a bending radius of 30 mm, demonstrating its potential for use in flexible sensor applications. This approach enables the fabrication of flexible and conformal electronics without requiring protective gases, using a cost-efficient laser system.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 524-531"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.066
Xuepeng Jiang , Li-Hsin Yeh , Mu’ayyad M. Al-Shrida , Jakob D. Hamilton , Beiwen Li , Iris V. Rivero , Andrea N. Camacho-Betancourt , Weijun Shen , Hantang Qin
Direct energy deposition (DED) is an emerging technology for remanufacturing as it enables fusion and deposition of metallic materials into complex geometries with high quality. The melting pool plays a critical role in quality control during the DED process. Ensuring stable melting pool geometry, temperature, and consistency is essential for producing defect-free components. Thermal imaging combined with unsupervised machine learning (ML) offers significant potential for in-situ defect prediction and quality control in the DED process. Moreover, in-situ thermal imaging generates incremental datasets, allowing for the continuous improvement of ML model predictions without the need for additional labelling as the dataset grows. In this work, we investigate the impact of self-organizing map (SOM)-based incremental learning parameters on in-situ thermal monitoring of the DED process using infrared (IR) imaging. Parameters including map size, neighborhood radius, learning rate, number of components, and the decay rate for neighborhood radius and learning rate were evaluated under low and high settings. Their effects on adjustment time for processing new IR images and final model accuracy, measured by quantization error (QE), were analysed. The findings provide a valuable starting point for researchers aiming to optimize SOM-based incremental learning for real-time defect detection using IR imaging of the DED melt pool.
{"title":"Impact of self organizing map based incremental learning parameters on in-situ IR melting pool imaging for direct energy deposition","authors":"Xuepeng Jiang , Li-Hsin Yeh , Mu’ayyad M. Al-Shrida , Jakob D. Hamilton , Beiwen Li , Iris V. Rivero , Andrea N. Camacho-Betancourt , Weijun Shen , Hantang Qin","doi":"10.1016/j.mfglet.2025.06.066","DOIUrl":"10.1016/j.mfglet.2025.06.066","url":null,"abstract":"<div><div>Direct energy deposition (DED) is an emerging technology for remanufacturing as it enables fusion and deposition of metallic materials into complex geometries with high quality. The melting pool plays a critical role in quality control during the DED process. Ensuring stable melting pool geometry, temperature, and consistency is essential for producing defect-free components. Thermal imaging combined with unsupervised machine learning (ML) offers significant potential for in-situ defect prediction and quality control in the DED process. Moreover, in-situ thermal imaging generates incremental datasets, allowing for the continuous improvement of ML model predictions without the need for additional labelling as the dataset grows. In this work, we investigate the impact of self-organizing map (SOM)-based incremental learning parameters on in-situ thermal monitoring of the DED process using infrared (IR) imaging. Parameters including map size, neighborhood radius, learning rate, number of components, and the decay rate for neighborhood radius and learning rate were evaluated under low and high settings. Their effects on adjustment time for processing new IR images and final model accuracy, measured by quantization error (QE), were analysed. The findings provide a valuable starting point for researchers aiming to optimize SOM-based incremental learning for real-time defect detection using IR imaging of the DED melt pool.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 559-565"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.068
Ross Zameroski , Michael Gomez , Tony Schmitz
This paper describes drill wear monitoring using the combination of a constrained-motion drilling dynamometer (CMDD) and aluminum witness sample. The drill wear state is assessed using the increase in torque and thrust force measured by the CMDD while drilling an aluminum witness sample. The drill is worn using the selected workpiece material and holes are intermittently drilled in the aluminum witness sample to determine the wear state. The hypothesis is that there is a direct link between the wear state and witness sample torque and thrust force increase, independent of the workpiece material. Therefore, a single model that relates tool wear to torque and thrust force increase can be calibrated and implemented for other drills and materials. The witness sample approach demonstrates good agreement between the predicted increase in force magnitude and the experimental results. A mechanistic drilling torque and thrust model is also described and a linear regression approach is defined to obtain the coefficients. The growth in these coefficients with drill wear state is examined and it is observed that one coefficient was highly sensitive to the wear state.
{"title":"Drill wear monitoring using a constrained-motion drilling dynamometer and aluminum witness sample","authors":"Ross Zameroski , Michael Gomez , Tony Schmitz","doi":"10.1016/j.mfglet.2025.06.068","DOIUrl":"10.1016/j.mfglet.2025.06.068","url":null,"abstract":"<div><div>This paper describes drill wear monitoring using the combination of a constrained-motion drilling dynamometer (CMDD) and aluminum witness sample. The drill wear state is assessed using the increase in torque and thrust force measured by the CMDD while drilling an aluminum witness sample. The drill is worn using the selected workpiece material and holes are intermittently drilled in the aluminum witness sample to determine the wear state. The hypothesis is that there is a direct link between the wear state and witness sample torque and thrust force increase, independent of the workpiece material. Therefore, a single model that relates tool wear to torque and thrust force increase can be calibrated and implemented for other drills and materials. The witness sample approach demonstrates good agreement between the predicted increase in force magnitude and the experimental results. A mechanistic drilling torque and thrust model is also described and a linear regression approach is defined to obtain the coefficients. The growth in these coefficients with drill wear state is examined and it is observed that one coefficient was highly sensitive to the wear state.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 576-587"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号