Pub Date : 2025-10-10DOI: 10.1016/j.mfglet.2025.09.002
Alice Proietti, Fabrizio Quadrini, Loredana Santo
Hybrid laminates were manufactured with a honeycomb interlayer of PETG between composite plies. The interlayer was obtained by 3D printing either on the machine bed (Hybrid-B) and on the prepreg surface (Hybrid-S). Compression molding was performed for consolidation. Hybrid-B exhibited an accumulation of PETG at the warp/weft intersection of the composite fabric while a more uniform distribution was shown by Hybrid-S. The bending strengths of Hybrid-B and Hybrid-S were 726 MPa and 718 MPa, respectively. Hybridization led to improvements in the damping behavior as the loss factor at room temperature increased of 55.7 % and 58.8 % for Hybrid-B and Hybrid-S, respectively.
{"title":"Manufacturing hybrid carbon fiber laminates with 3D printed interlayers","authors":"Alice Proietti, Fabrizio Quadrini, Loredana Santo","doi":"10.1016/j.mfglet.2025.09.002","DOIUrl":"10.1016/j.mfglet.2025.09.002","url":null,"abstract":"<div><div>Hybrid laminates were manufactured with a honeycomb interlayer of PETG between composite plies. The interlayer was obtained by 3D printing either on the machine bed (Hybrid-B) and on the prepreg surface (Hybrid-S). Compression molding was performed for consolidation. Hybrid-B exhibited an accumulation of PETG at the warp/weft intersection of the composite fabric while a more uniform distribution was shown by Hybrid-S. The bending strengths of Hybrid-B and Hybrid-S were 726 MPa and 718 MPa, respectively. Hybridization led to improvements in the damping behavior as the loss factor at room temperature increased of 55.7 % and 58.8 % for Hybrid-B and Hybrid-S, respectively.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 21-24"},"PeriodicalIF":2.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325779","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}
The Facilities Layout Problem involves arranging facilities within a given space to achieve specific objectives, such as minimizing transportation costs or reducing energy consumption. This issue arises in advanced manufacturing, particularly in Reconfigurable Manufacturing Systems (RMS), which allow layout adjustments based on changing product mixes, volumes, or processes. This paper compares the Double Dueling Deep Q-Network with traditional Q-learning and simulated annealing metaheuristic to assess the effectiveness of Deep Reinforcement Learning in addressing such challenges. Specifically, the study evaluates DDDQN performance in interactive environments where workstations are represented using a discrete approach, highlighting the role of reconfigurability in adjusting workstation implantation, orientation, and pickup/drop-off locations as required in RMS.
{"title":"Contribution of deep reinforcement learning to solve reconfigurable facilities layout problems","authors":"Amine Chiboub , Julien Francois , Thècle Alix , Rémy Dupas","doi":"10.1016/j.mfglet.2025.09.003","DOIUrl":"10.1016/j.mfglet.2025.09.003","url":null,"abstract":"<div><div>The Facilities Layout Problem involves arranging facilities within a given space to achieve specific objectives, such as minimizing transportation costs or reducing energy consumption. This issue arises in advanced manufacturing, particularly in Reconfigurable Manufacturing Systems (RMS), which allow layout adjustments based on changing product mixes, volumes, or processes. This paper compares the Double Dueling Deep Q-Network with traditional Q-learning and simulated annealing metaheuristic to assess the effectiveness of Deep Reinforcement Learning in addressing such challenges. Specifically, the study evaluates DDDQN performance in interactive environments where workstations are represented using a discrete approach, highlighting the role of reconfigurability in adjusting workstation implantation, orientation, and pickup/drop-off locations as required in RMS.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 16-20"},"PeriodicalIF":2.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325780","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 : 2025-09-12DOI: 10.1016/j.mfglet.2025.09.001
Russel Bradley, Stanley S. Salim, Brian W. Anthony
This study demonstrates how low-code/no-code (LCNC) platforms can enable undergraduate students without software development backgrounds to design and build digital manufacturing systems. Students developed an IoT-enabled Manufacturing Execution System using Tulip Interfaces—an LCNC platform, focusing on applications like inventory tracking, machine monitoring, and digital work instructions in the FrED Factory—a learning factory at MIT. Evaluation through a pilot study showed students gained a strong understanding of smart manufacturing concepts while spending most of their time on systems design rather than software development. Individual interviews followed by a post-interview survey highlighted that the average percentage of time split between systems design and debugging the LCNC platform was 70–30% respectively. Additionally, all students responded with “strongly agree” to the question of whether the project enhanced their understanding of smart manufacturing concepts. LCNC platforms offer a practical, accessible approach to teaching digital manufacturing and can accelerate skill development in both educational and industrial settings.
{"title":"Learning through development of a digital manufacturing system in a learning factory using low-code/no-code platforms","authors":"Russel Bradley, Stanley S. Salim, Brian W. Anthony","doi":"10.1016/j.mfglet.2025.09.001","DOIUrl":"10.1016/j.mfglet.2025.09.001","url":null,"abstract":"<div><div>This study demonstrates how low-code/no-code (LCNC) platforms can enable undergraduate students without software development backgrounds to design and build digital manufacturing systems. Students developed an IoT-enabled Manufacturing Execution System using Tulip Interfaces—an LCNC platform, focusing on applications like inventory tracking, machine monitoring, and digital work instructions in the FrED Factory—a learning factory at MIT. Evaluation through a pilot study showed students gained a strong understanding of smart manufacturing concepts while spending most of their time on systems design rather than software development. Individual interviews followed by a post-interview survey highlighted that the average percentage of time split between systems design and debugging the LCNC platform was 70–30% respectively. Additionally, all students responded with “strongly agree” to the question of whether the project enhanced their understanding of smart manufacturing concepts. LCNC platforms offer a practical, accessible approach to teaching digital manufacturing and can accelerate skill development in both educational and industrial settings.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 10-15"},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158856","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 : 2025-09-03DOI: 10.1016/j.mfglet.2025.08.005
Grzegorz Miebs , Rafał A. Bachorz
Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 s making planning across varied product types easier.
{"title":"Technology prediction of a 3D model using neural network","authors":"Grzegorz Miebs , Rafał A. Bachorz","doi":"10.1016/j.mfglet.2025.08.005","DOIUrl":"10.1016/j.mfglet.2025.08.005","url":null,"abstract":"<div><div>Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 s making planning across varied product types easier.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 5-9"},"PeriodicalIF":2.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019286","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 : 2025-09-01DOI: 10.1016/j.mfglet.2025.08.004
Austin Clark , Ihab Ragai
A Taguchi L9 orthogonal array and Analysis of Variance (ANOVA) test for equal variance were used to determine variation in torque when adaptive torque monitoring and control is used in a Friction Stir Welding (FSW) application on AA6061-T6. Standard deviation was analyzed against the parameters of Programmed Torque (PT) and Feed Rate (FR). PT for the Z-axis motor determined the axial force at the tool during welding. PT values of 35, 40 and 45 Nm and FR of 100, 200 and 300 mm/min were studied in this paper. PT values of 35, 40 and 45 Nm correlated to 7.33, 8.38 and 9.43 kN axial force, respectively. It was found that the optimal parameter set with the lowest variation in torque through the entirety of the weld was conducted with a PT (45 Nm/9.43 kN) and an FR of 100 mm/min. These were the maximum and minimum values for PT and FR, respectively. Higher levels of torque variation occurred with higher FR and lower PT. This study offers insight into the effects process parameters have on torque variation when adaptive torque monitoring and control is used.
{"title":"Utilizing Taguchi and ANOVA methods to investigate standard deviation of programmed torque for aluminum 6061-T6 friction stir welding with adaptive torque monitoring and control","authors":"Austin Clark , Ihab Ragai","doi":"10.1016/j.mfglet.2025.08.004","DOIUrl":"10.1016/j.mfglet.2025.08.004","url":null,"abstract":"<div><div>A Taguchi L<sub>9</sub> orthogonal array and Analysis of Variance (ANOVA) test for equal variance were used to determine variation in torque when adaptive torque monitoring and control is used in a Friction Stir Welding (FSW) application on AA6061-T6. Standard deviation was analyzed against the parameters of Programmed Torque (PT) and Feed Rate (FR). PT for the Z-axis motor determined the axial force at the tool during welding. PT values of 35, 40 and 45 Nm and FR of 100, 200 and 300 mm/min were studied in this paper. PT values of 35, 40 and 45 Nm correlated to 7.33, 8.38 and 9.43 kN axial force, respectively. It was found that the optimal parameter set with the lowest variation in torque through the entirety of the weld was conducted with a PT (45 Nm/9.43 kN) and an FR of 100 mm/min. These were the maximum and minimum values for PT and FR, respectively. Higher levels of torque variation occurred with higher FR and lower PT. This study offers insight into the effects process parameters have on torque variation when adaptive torque monitoring and control is used.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 1-4"},"PeriodicalIF":2.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019285","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 : 2025-08-22DOI: 10.1016/j.mfglet.2025.08.002
Rafael Guerra Silva , Gustavo Morales Pavez , Luis F. Caminos
Additive manufacturing of continuous fiber-reinforced polymer composites faces challenges in achieving consistent flexural strength and stiffness. Additively manufactured sandwich structures with continuous fiber reinforcement were produced in different batches and subjected to flexural tests. The production replicated real-world conditions, including filament spool changes, fiber aging, and time gaps between batches. The mechanical properties were consistent in early batches, but variability in flexural strength and stiffness increased from one batch to the next, reaching deviations up to 60% for glass fiber and 70% for carbon fiber in later batches. Although the dual-head additive manufacturing system protects the polymer filament from humidity during the sequential fiber deposition process and waiting periods, similar provisions are also necessary for the reinforcement filament to minimize or eliminate polymer-fiber interlayer debonding.
{"title":"Reliability challenges in additive manufacturing of continuous fiber-reinforced sandwich structures","authors":"Rafael Guerra Silva , Gustavo Morales Pavez , Luis F. Caminos","doi":"10.1016/j.mfglet.2025.08.002","DOIUrl":"10.1016/j.mfglet.2025.08.002","url":null,"abstract":"<div><div>Additive manufacturing of continuous fiber-reinforced polymer composites faces challenges in achieving consistent flexural strength and stiffness. Additively manufactured sandwich structures with continuous fiber reinforcement were produced in different batches and subjected to flexural tests. The production replicated real-world conditions, including filament spool changes, fiber aging, and time gaps between batches. The mechanical properties were consistent in early batches, but variability in flexural strength and stiffness increased from one batch to the next, reaching deviations up to 60% for glass fiber and 70% for carbon fiber in later batches. Although the dual-head additive manufacturing system protects the polymer filament from humidity during the sequential fiber deposition process and waiting periods, similar provisions are also necessary for the reinforcement filament to minimize or eliminate polymer-fiber interlayer debonding.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"45 ","pages":"Pages 112-115"},"PeriodicalIF":2.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902889","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}
Artificial intelligence (AI) offers promise for advancing composite manufacturing by enhancing process monitoring, efficiency, and quality while mitigating defects. Nevertheless, AI application for anomaly detection is constrained by limited real-world data and reliance on labeled datasets, necessitating frequent retraining. We propose a novel three-stage anomaly detection framework for composite curing. First, an autoencoder is trained on normal data to extract features. Next, K-means clustering groups similar patterns. Finally, a model combining Mahalanobis distance with an elliptic envelope quantifies deviations using cluster-specific thresholds. Evaluation on autoclave data with a Digital Image Correlation setup yielded an impressive detection accuracy of 99.69% overall.
{"title":"Unsupervised anomaly detection in composite manufacturing using autoencoders and cluster-specific thresholding","authors":"Deepak Kumar, Pragathi Chan Agraharam, Sirish Namilae","doi":"10.1016/j.mfglet.2025.08.001","DOIUrl":"10.1016/j.mfglet.2025.08.001","url":null,"abstract":"<div><div>Artificial intelligence (AI) offers promise for advancing composite manufacturing by enhancing process monitoring, efficiency, and quality while mitigating defects. Nevertheless, AI application for anomaly detection is constrained by limited real-world data and reliance on labeled datasets, necessitating frequent retraining. We propose a novel three-stage anomaly detection framework for composite curing. First, an autoencoder is trained on normal data to extract features. Next, K-means clustering groups similar patterns. Finally, a model combining Mahalanobis distance with an elliptic envelope quantifies deviations using cluster-specific thresholds. Evaluation on autoclave data with a Digital Image Correlation setup yielded an impressive detection accuracy of 99.69% overall.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"45 ","pages":"Pages 101-106"},"PeriodicalIF":2.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902875","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 : 2025-08-19DOI: 10.1016/j.mfglet.2025.08.003
Abhishek Kumar , Rajan Singh , Soumen Mandal , Gayatri Paul , Barnali Maji , Manab Mallik
The direct ink writing (DIW) technique prints alumina Pyramidoids. Ink formulation included the usage of pure alumina powder, phenolic resin, and deionized water. Alumina ink with a solid loading of 64 vol% provides suitable rheological properties for 3D printing. The synthesized ink was used for 3D printing of a pyramidoid and sintering at different temperatures (1500 °C–1600 °C). The sample sintered at 1600 °C exhibits a dense microstructure (98 %), good flexural strength (308.34 ± 10 MPa), moderate fracture toughness (4.01 ± 0.4 MPa.m1/2), and high hardness (1625 HV).
{"title":"Structure-property correlation of alumina pyramidoids fabricated by direct ink writing","authors":"Abhishek Kumar , Rajan Singh , Soumen Mandal , Gayatri Paul , Barnali Maji , Manab Mallik","doi":"10.1016/j.mfglet.2025.08.003","DOIUrl":"10.1016/j.mfglet.2025.08.003","url":null,"abstract":"<div><div>The direct ink writing (DIW) technique prints alumina Pyramidoids. Ink formulation included the usage of pure alumina powder, phenolic resin, and deionized water. Alumina ink with a solid loading of 64 vol% provides suitable rheological properties for 3D printing. The synthesized ink was used for 3D printing of a pyramidoid and sintering at different temperatures (1500 °C–1600 °C). The sample sintered at 1600 °C exhibits a dense microstructure (98 %), good flexural strength (308.34 ± 10 MPa), moderate fracture toughness (4.01 ± 0.4 MPa.m<sup>1/2</sup>), and high hardness (1625 HV).</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"45 ","pages":"Pages 107-111"},"PeriodicalIF":2.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902888","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 : 2025-08-12DOI: 10.1016/j.mfglet.2025.07.007
Erik Denlinger, Zoe Michaleris , Tyler Nelson
This study evaluates the effect of accounting for powder in mechanical predictions for laser powder-bed-fusion by comparing: an inherent-strain based mechanical-only analysis, and thermomechanical simulations where the thermal analysis is conducted with and without powder elements. Results on Inconel 718 parts show that the thermal predictions with powder elements have less than 7 % error while the thermal predictions without powder elements could not capture the trend in measurements. In predicting the peak distortion, the thermomechanical model with powder elements has 21 % lower prediction error than the model without powder elements and 30 % lower prediction error than the mechanical-only analysis.
{"title":"Effect of accounting for powder in thermomechanical simulations for laser powder bed fusion","authors":"Erik Denlinger, Zoe Michaleris , Tyler Nelson","doi":"10.1016/j.mfglet.2025.07.007","DOIUrl":"10.1016/j.mfglet.2025.07.007","url":null,"abstract":"<div><div>This study evaluates the effect of accounting for powder in mechanical predictions for laser powder-bed-fusion by comparing: an inherent-strain based mechanical-only analysis, and thermomechanical simulations where the thermal analysis is conducted with and without powder elements. Results on Inconel 718 parts show that the thermal predictions with powder elements have less than 7 % error while the thermal predictions without powder elements could not capture the trend in measurements. In predicting the peak distortion, the thermomechanical model with powder elements has 21 % lower prediction error than the model without powder elements and 30 % lower prediction error than the mechanical-only analysis.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"45 ","pages":"Pages 93-100"},"PeriodicalIF":2.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863842","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.2025.06.064
Matthew Williams , Ashish Jacob , Guha Manogharan
Hybrid additive manufacturing can be troublesome when implementing new processes using existing hardware which makes this research field increasingly prominent. Even after the integration of crucial hardware components necessary for Hybrid AM, there is no suitable procedure capable of efficient toolpath planning of multi-material and multi-functional materials. Topology-optimized design would benefit from a planned process using hybrid manufacturing with simple electronics integration because of the need for optimized functional structures in the aerospace and automotive industries. This research takes multi-axis CNC toolpath strategies for syringe-deposited conductive inks over additively manufactured topology-optimized structures and uses a hybrid AM machine utilizing multiple tools for manufacturing curvilinear traces over the drone frame design. Process parameter configuration for machine hardware and CAM toolpath tolerancing with machine simulations are discussed in this paper. The ability to route conductive traces over topologically optimized structures has been studied and implemented using a 5-axis toolpath planning strategy. The challenge lies in the ability to deposit traces seamlessly across conformal surfaces. The study demonstrates that the manufactured traces were seamless with no breakages, and the measured resistances across the trenches varied between 53.5 and 134.04 Ω.
{"title":"Continuous 5-axis routing of syringe deposited conductive traces over topology optimized structures","authors":"Matthew Williams , Ashish Jacob , Guha Manogharan","doi":"10.1016/j.mfglet.2025.06.064","DOIUrl":"10.1016/j.mfglet.2025.06.064","url":null,"abstract":"<div><div>Hybrid additive manufacturing can be troublesome when implementing new processes using existing hardware which makes this research field increasingly prominent. Even after the integration of crucial hardware components necessary for Hybrid AM, there is no suitable procedure capable of efficient toolpath planning of multi-material and multi-functional materials. Topology-optimized design would benefit from a planned process using hybrid manufacturing with simple electronics integration because of the need for optimized functional structures in the aerospace and automotive industries. This research takes multi-axis CNC toolpath strategies for syringe-deposited conductive inks over additively manufactured topology-optimized structures and uses a hybrid AM machine utilizing multiple tools for manufacturing curvilinear traces over the drone frame design. Process parameter configuration for machine hardware and CAM toolpath tolerancing with machine simulations are discussed in this paper. The ability to route conductive traces over topologically optimized structures has been studied and implemented using a 5-axis toolpath planning strategy. The challenge lies in the ability to deposit traces seamlessly across conformal surfaces. The study demonstrates that the manufactured traces were seamless with no breakages, and the measured resistances across the trenches varied between 53.5 and 134.04 Ω.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 540-551"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926540","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}
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