Abstract While reactor wall preconditioning was previously shown to influence the yield in chemical vapor deposition (CVD), especially for coatings of carbon nanotubes (CNTs), it was limited to studying accumulating deposits over a number of runs. However, the effects of temperature and duration as the reactor walls are exposed to hot humidity for an extended period of time between growth runs was not previously studied systematically. Here, we combine experimental measurements with a mathematical model to elucidate how thermochemical history of reactor walls impacts growth yield of vertically aligned CNT films. Importantly, we demonstrate one-order-of-magnitude higher CNT yield, by increasing the interim, i.e., the time between runs. We explain the results based on previously unexplored process sensitivity to trace amounts of oxygen-containing species in the reactor. In particular, we model the effect of small amounts of water vapor desorbing from reactor walls during growth. Our results reveal the outgassing dynamics, and show the underlying mechanism of generating growth promoting molecules. By installing a humidity sensor in our custom-designed multizone rapid thermal CVD reactor, we are able to uniquely correlate the amount of moisture within the reactor to real-time measurements of growth kinetics, as well as ex situ characterization of CNT alignment and atomic defects. Our findings enable a scientifically grounded approach toward both boosting growth yield and improving its consistency by reducing run-to-run variations. Accordingly, engineered dynamics recipes can be envisioned to leverage this effect for improving manufacturing process scalability and robustness.
{"title":"Order-of-Magnitude Increase in Carbon Nanotube Yield Based on Modeling Transient Diffusion and Outgassing of Water from Reactor Walls","authors":"Golnaz Tomaraei, Moataz Abdulhafez, Mostafa Bedewy","doi":"10.1115/1.4063965","DOIUrl":"https://doi.org/10.1115/1.4063965","url":null,"abstract":"Abstract While reactor wall preconditioning was previously shown to influence the yield in chemical vapor deposition (CVD), especially for coatings of carbon nanotubes (CNTs), it was limited to studying accumulating deposits over a number of runs. However, the effects of temperature and duration as the reactor walls are exposed to hot humidity for an extended period of time between growth runs was not previously studied systematically. Here, we combine experimental measurements with a mathematical model to elucidate how thermochemical history of reactor walls impacts growth yield of vertically aligned CNT films. Importantly, we demonstrate one-order-of-magnitude higher CNT yield, by increasing the interim, i.e., the time between runs. We explain the results based on previously unexplored process sensitivity to trace amounts of oxygen-containing species in the reactor. In particular, we model the effect of small amounts of water vapor desorbing from reactor walls during growth. Our results reveal the outgassing dynamics, and show the underlying mechanism of generating growth promoting molecules. By installing a humidity sensor in our custom-designed multizone rapid thermal CVD reactor, we are able to uniquely correlate the amount of moisture within the reactor to real-time measurements of growth kinetics, as well as ex situ characterization of CNT alignment and atomic defects. Our findings enable a scientifically grounded approach toward both boosting growth yield and improving its consistency by reducing run-to-run variations. Accordingly, engineered dynamics recipes can be envisioned to leverage this effect for improving manufacturing process scalability and robustness.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"73 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135321439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability.
{"title":"AUTOMATED EVALUATION AND RATING OF PRODUCT REPAIRABILITY USING ARTIFICIAL INTELLIGENCE-BASED APPROACHES","authors":"Hao-Yu Liao, Behzad Esmaeilian, Sara Behdad","doi":"10.1115/1.4063561","DOIUrl":"https://doi.org/10.1115/1.4063561","url":null,"abstract":"Abstract Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"76 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136371683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuoran Li, Dianping Zhang, Ruiming Chen, Songlin Wang, Yu-Jun Xia, Ming Lou, YongBing Li
Abstract Press-hardened steel (PHS) with extremely high strength has wide applications in vehicle body manufacturing as an innovative lightweight material. However, the poor weldability of PHS results in poor weld toughness and a high risk of interfacial fracture, posing challenges to the resistance spot welding (RSW) process. Introducing an external magnetic field in the welding process to perform electromagnetic stirring (EMS), magnetically assisted RSW (MA-RSW) process has been proven an effective method to improve the weld toughness of high-strength steel, but it may increase the risk of expulsion. In this study, a new process called SPMA-RSW is developed to improve the weldability of PHS by combining MA-RSW and the stepped-current pulses (SP) technique, which can enlarge the weld lobe. Nugget appearance, microstructure, microhardness, and mechanical properties were systematically investigated by comparing traditional RSW, MA-RSW, SP-RSW, and SPMA-RSW. The result showed that the SPMA-RSW process would significantly increase the nugget size, inhibit the shrinkage voids, finer the grain, and harden the nugget. This increased the lap-shear strength, energy absorption, and changed the fracture mode from brittle interfacial (IF) mode to ductile plug fracture (PF) mode at the same heat input. Then, a simple model was developed to reveal the mechanism of the effect of EMS on the fracture mode transition and was verified by experiment. This work can help improve the weld quality and thermal efficiency of the RSW process for PHS.
{"title":"Improving Weldability of Press Hardened Steel through Combining Stepped Current Pulse and Magnetically Assisted Resistance Spot Welding Process","authors":"Zhuoran Li, Dianping Zhang, Ruiming Chen, Songlin Wang, Yu-Jun Xia, Ming Lou, YongBing Li","doi":"10.1115/1.4063904","DOIUrl":"https://doi.org/10.1115/1.4063904","url":null,"abstract":"Abstract Press-hardened steel (PHS) with extremely high strength has wide applications in vehicle body manufacturing as an innovative lightweight material. However, the poor weldability of PHS results in poor weld toughness and a high risk of interfacial fracture, posing challenges to the resistance spot welding (RSW) process. Introducing an external magnetic field in the welding process to perform electromagnetic stirring (EMS), magnetically assisted RSW (MA-RSW) process has been proven an effective method to improve the weld toughness of high-strength steel, but it may increase the risk of expulsion. In this study, a new process called SPMA-RSW is developed to improve the weldability of PHS by combining MA-RSW and the stepped-current pulses (SP) technique, which can enlarge the weld lobe. Nugget appearance, microstructure, microhardness, and mechanical properties were systematically investigated by comparing traditional RSW, MA-RSW, SP-RSW, and SPMA-RSW. The result showed that the SPMA-RSW process would significantly increase the nugget size, inhibit the shrinkage voids, finer the grain, and harden the nugget. This increased the lap-shear strength, energy absorption, and changed the fracture mode from brittle interfacial (IF) mode to ductile plug fracture (PF) mode at the same heat input. Then, a simple model was developed to reveal the mechanism of the effect of EMS on the fracture mode transition and was verified by experiment. This work can help improve the weld quality and thermal efficiency of the RSW process for PHS.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"33 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136316724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This Special Issue serves as a bridge between the ASME Journal of Manufacturing Science and Engineering (JMSE) and the global community of manufacturing researchers. Its primary objective is to curate a collection of high-level scientific articles that push the boundaries of knowledge in the realm of Human-Robot Collaboration (HRC) for forward-looking, human-centric smart manufacturing. It encourages researchers to present their innovative methodologies, tools, systems, and practical case studies, fostering advancements that integrate cognitive computing, mixed reality, and advanced data analytics. By emphasizing proactive teamwork and seamless interaction, this initiative aims to narrow the gap between human operators and industrial robots. Contributions are sought in areas such as cognitive HRC systems, safety considerations, adaptive motion planning, human intention prediction, and semantic knowledge representation—key components in achieving efficient and effective collaboration within the manufacturing industry. Beyond its scientific impact, this Special Issue also seeks to unite leading scientific communities worldwide.
{"title":"Special Issue on Human-Robot Collaboration for Futuristic Human-Centric Smart Manufacturing","authors":"Pai Zheng","doi":"10.1115/1.4063447","DOIUrl":"https://doi.org/10.1115/1.4063447","url":null,"abstract":"This Special Issue serves as a bridge between the ASME Journal of Manufacturing Science and Engineering (JMSE) and the global community of manufacturing researchers. Its primary objective is to curate a collection of high-level scientific articles that push the boundaries of knowledge in the realm of Human-Robot Collaboration (HRC) for forward-looking, human-centric smart manufacturing. It encourages researchers to present their innovative methodologies, tools, systems, and practical case studies, fostering advancements that integrate cognitive computing, mixed reality, and advanced data analytics. By emphasizing proactive teamwork and seamless interaction, this initiative aims to narrow the gap between human operators and industrial robots. Contributions are sought in areas such as cognitive HRC systems, safety considerations, adaptive motion planning, human intention prediction, and semantic knowledge representation—key components in achieving efficient and effective collaboration within the manufacturing industry. Beyond its scientific impact, this Special Issue also seeks to unite leading scientific communities worldwide.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Yang, Ying Cai, Yeo Jung Yoon, Hangbo Zhao, Satyandra K. Gupta
Abstract Robotic manipulators can be used to deposit materials on non-planar surfaces. Conventional sensor-based industrial robots can only work on stationary surfaces, relying on the scanned data prior to printing. As a result, performing depositions that involve changes in plane motion presents significant challenges. The deposition of conformal materials on a time-varying deformable surface requires the manipulators to update coordinates in real time on the plane for positioning and orientation. This can be achieved by employing multiple sensors for manipulator motion planning and control, in order to prevent collisions between the tool and the surface. In this paper, we propose simple tool center point calibration, initial point coordinate estimation, and a gap compensation scheme to combine real-time feedback control and direct conformal deposition. Combining these elements allows us to maintain a controlled gap between the tooltip and the deformable surface during the deposition. We test the efficacy of the proposed approach by printing a single layer of ink patterns with approximately 950 μm line width on a deformable surface. We also characterize the printing quality with different gaps and printing steps and show that sensor-based control is critical in smooth printing. Finally, the effects of changing the relative position of the tooltip, different surface colors, and laser sensor position are characterized.
{"title":"SENSOR-BASED PLANNING AND CONTROL FOR CONFORMAL DEPOSITION ON A DEFORMABLE SURFACE USING AN ARTICULATED INDUSTRIAL ROBOT","authors":"Yang Yang, Ying Cai, Yeo Jung Yoon, Hangbo Zhao, Satyandra K. Gupta","doi":"10.1115/1.4063560","DOIUrl":"https://doi.org/10.1115/1.4063560","url":null,"abstract":"Abstract Robotic manipulators can be used to deposit materials on non-planar surfaces. Conventional sensor-based industrial robots can only work on stationary surfaces, relying on the scanned data prior to printing. As a result, performing depositions that involve changes in plane motion presents significant challenges. The deposition of conformal materials on a time-varying deformable surface requires the manipulators to update coordinates in real time on the plane for positioning and orientation. This can be achieved by employing multiple sensors for manipulator motion planning and control, in order to prevent collisions between the tool and the surface. In this paper, we propose simple tool center point calibration, initial point coordinate estimation, and a gap compensation scheme to combine real-time feedback control and direct conformal deposition. Combining these elements allows us to maintain a controlled gap between the tooltip and the deformable surface during the deposition. We test the efficacy of the proposed approach by printing a single layer of ink patterns with approximately 950 μm line width on a deformable surface. We also characterize the printing quality with different gaps and printing steps and show that sensor-based control is critical in smooth printing. Finally, the effects of changing the relative position of the tooltip, different surface colors, and laser sensor position are characterized.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lily Raymond, Weijian Hua, Naima Valentin, Ryan Coulter, Erick Bandala, Kaitlin Leong, Jada Okaikoi, Yifei Jin
Abstract Creating multilayered channels for mimicking human blood vessels in thick tissues is the main challenge to overcome in organ biofabrication. Current three-dimensional (3D) printing strategies cannot effectively manufacture hollow channels with multiple layers. This study aims to propose a coaxial nozzle-assisted embedded 3D printing method in which core–shell filaments can be formed in a yield-stress matrix bath by extruding different ink materials through the corresponding channels. The materials selected for the core ink, shell ink, and matrix bath are Pluronic F127 (F127) and calcium chloride (CaCl2), sodium alginate (NaAlg), and poly(ethylene glycol) diacrylate (PEGDA) and nanoclay, respectively. After crosslinking the matrix bath and shell, the core layer made from the sacrificial ink (F127) is removed to generate a single-layered, hollow channel. In this work, the effects of ink material properties and operating conditions on core–shell filament formation have been systematically studied. The rheological and mechanical properties of the yield-stress matrix bath have been characterized as well. A thick tissue-like structure with embedded single-layered, hollow channels has been successfully printed for demonstration. Since it is feasible to design coaxial nozzles with a core–shell–shell architecture, the proposed method is technically extendable to create double-layered channels within a cellular tissue construct, accurately mimicking human blood vascular networks in thick tissues in the future.
{"title":"Coaxial Nozzle-Assisted Embedded 3D Printing of Single-Layered Channels Within a Yield-Stress Matrix Bath","authors":"Lily Raymond, Weijian Hua, Naima Valentin, Ryan Coulter, Erick Bandala, Kaitlin Leong, Jada Okaikoi, Yifei Jin","doi":"10.1115/1.4063452","DOIUrl":"https://doi.org/10.1115/1.4063452","url":null,"abstract":"Abstract Creating multilayered channels for mimicking human blood vessels in thick tissues is the main challenge to overcome in organ biofabrication. Current three-dimensional (3D) printing strategies cannot effectively manufacture hollow channels with multiple layers. This study aims to propose a coaxial nozzle-assisted embedded 3D printing method in which core–shell filaments can be formed in a yield-stress matrix bath by extruding different ink materials through the corresponding channels. The materials selected for the core ink, shell ink, and matrix bath are Pluronic F127 (F127) and calcium chloride (CaCl2), sodium alginate (NaAlg), and poly(ethylene glycol) diacrylate (PEGDA) and nanoclay, respectively. After crosslinking the matrix bath and shell, the core layer made from the sacrificial ink (F127) is removed to generate a single-layered, hollow channel. In this work, the effects of ink material properties and operating conditions on core–shell filament formation have been systematically studied. The rheological and mechanical properties of the yield-stress matrix bath have been characterized as well. A thick tissue-like structure with embedded single-layered, hollow channels has been successfully printed for demonstration. Since it is feasible to design coaxial nozzles with a core–shell–shell architecture, the proposed method is technically extendable to create double-layered channels within a cellular tissue construct, accurately mimicking human blood vascular networks in thick tissues in the future.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vivian Wong, Sang Hun Kim, Junyoung Park, Jinkyoo Park, Kincho Law
Abstract The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions. Subjected to random disruptions due to machine malfunction or maintenance, industry production settings often choose to adopt dispatching rules to enable adaptive, real-time re-scheduling, rather than traditional methods that require costly re-computation on the new configuration every time the problem condition changes dynamically. To generate dispatching rules for the ISBJSSP problem, we introduce a dynamic disjunctive graph formulation characterized by nodes and edges subjected to continuous deletions and additions. This formulation enables the training of an adaptive scheduler utilizing graph neural networks and reinforcement learning. Furthermore, a simulator is developed to simulate interruption, swapping, and blocking in the ISBJSSP setting. By employing a set of reported benchmark instances, we conduct a detailed experimental study on ISBJSSP instances with a range of machine shutdown probabilities to show that the scheduling policies generated can outperform or are at least as competitive as existing dispatching rules with predetermined priority. This study shows that the ISBJSSP, which requires real-time adaptive solutions, can be scheduled efficiently with the proposed method when production interruptions occur with random machine shutdowns.
{"title":"GENERATING DISPATCHING RULES FOR THE INTERRUPTING SWAP-ALLOWED BLOCKING JOB SHOP PROBLEM USING GRAPH NEURAL NETWORK AND REINFORCEMENT LEARNING","authors":"Vivian Wong, Sang Hun Kim, Junyoung Park, Jinkyoo Park, Kincho Law","doi":"10.1115/1.4063652","DOIUrl":"https://doi.org/10.1115/1.4063652","url":null,"abstract":"Abstract The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions. Subjected to random disruptions due to machine malfunction or maintenance, industry production settings often choose to adopt dispatching rules to enable adaptive, real-time re-scheduling, rather than traditional methods that require costly re-computation on the new configuration every time the problem condition changes dynamically. To generate dispatching rules for the ISBJSSP problem, we introduce a dynamic disjunctive graph formulation characterized by nodes and edges subjected to continuous deletions and additions. This formulation enables the training of an adaptive scheduler utilizing graph neural networks and reinforcement learning. Furthermore, a simulator is developed to simulate interruption, swapping, and blocking in the ISBJSSP setting. By employing a set of reported benchmark instances, we conduct a detailed experimental study on ISBJSSP instances with a range of machine shutdown probabilities to show that the scheduling policies generated can outperform or are at least as competitive as existing dispatching rules with predetermined priority. This study shows that the ISBJSSP, which requires real-time adaptive solutions, can be scheduled efficiently with the proposed method when production interruptions occur with random machine shutdowns.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135666691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The process parameters of Directed Energy Deposition (DED) have been widely studied including laser power, powder flow rate, and scanning speed. These parameters affect clad dimension and melt pool temperature, which are directly related to part quality. However, laser/powder profiles and their alignment have obtained less attention due to the cumbersome characterization process, although they can be directly associated with local energy density for melt pool formation. This study examines the impact of the alignment between the laser beam and powder flow distributions in DED on clad dimension and melt pool temperature. The laser beam and powder profiles are characterized by measuring their respective 2D Gaussian profiles for a given standoff distance. Aligned and misaligned laser-powder profiles are then used to build single-clad square geometries. It was found that a 500-µm offset between the centers of the laser and powder profiles causes up to a 20% change in both the width and the height of a single clad as well as an average temperature increase of 100 K. To understand the interaction between powder flow, energy flux, and local temperature, the local specific energy density distribution was plotted in 2D. These results suggest that laser-powder misalignment may significantly alter the thermal history and shape of deposited clads, possibly preventing DED-manufactured parts from meeting design properties and causing build failures.
{"title":"Effects of Laser-Powder Alignment on Clad Dimension and Melt Pool Temperature in Directed Energy Deposition","authors":"Jihoon Jeong, Samantha Webster, Rujing Zha, Jon-Erik Mogonye, Kornel Ehmann, Jian Cao","doi":"10.1115/1.4063390","DOIUrl":"https://doi.org/10.1115/1.4063390","url":null,"abstract":"Abstract The process parameters of Directed Energy Deposition (DED) have been widely studied including laser power, powder flow rate, and scanning speed. These parameters affect clad dimension and melt pool temperature, which are directly related to part quality. However, laser/powder profiles and their alignment have obtained less attention due to the cumbersome characterization process, although they can be directly associated with local energy density for melt pool formation. This study examines the impact of the alignment between the laser beam and powder flow distributions in DED on clad dimension and melt pool temperature. The laser beam and powder profiles are characterized by measuring their respective 2D Gaussian profiles for a given standoff distance. Aligned and misaligned laser-powder profiles are then used to build single-clad square geometries. It was found that a 500-µm offset between the centers of the laser and powder profiles causes up to a 20% change in both the width and the height of a single clad as well as an average temperature increase of 100 K. To understand the interaction between powder flow, energy flux, and local temperature, the local specific energy density distribution was plotted in 2D. These results suggest that laser-powder misalignment may significantly alter the thermal history and shape of deposited clads, possibly preventing DED-manufactured parts from meeting design properties and causing build failures.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"12 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guest Editorial 2023 Manufacturing Science and Engineering Conference Accepted Manuscript Binil Starly, Binil Starly 5999 S Backus Mall Mesa, AZ 85212 Email: binil.starly@asu.edu Search for other works by this author on: This Site PubMed Google Scholar Albert Shih Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125 Email: shiha@umich.edu Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information Binil Starly 5999 S Backus Mall Mesa, AZ 85212 Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125 Email: shiha@umich.edu Email: binil.starly@asu.edu J. Manuf. Sci. Eng. 1-2 (2 pages) Paper No: MANU-23-1588 https://doi.org/10.1115/1.4063521 Published Online: September 27, 2023 Article history Received: September 22, 2023 Revised: September 23, 2023 Accepted: September 23, 2023 Published: September 27, 2023
客座编辑2023制造科学与工程会议接受手稿Binil Starly, Binil Starly 5999 S Backus Mall Mesa, AZ 85212电子邮件:binil.starly@asu.edu搜索作者的其他作品在:本网站PubMed谷歌学者Albert Shih Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125电子邮件:shiha@umich.edu搜索作者的其他作品在:该网站PubMed谷歌学者作者和文章信息Binil Starly 5999 S Backus Mall Mesa, AZ 85212 Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125 Email: shiha@umich.edu Email: binil.starly@asu.edu J. Manuf. Sci。论文编号:MANU-23-1588 https://doi.org/10.1115/1.4063521发表时间:2023年9月27日收稿时间:2023年9月22日修稿时间:2023年9月23日收稿时间:2023年9月23日发表时间:2023年9月27日
{"title":"2023 Manufacturing Science and Engineering Conference","authors":"Binil Starly, Albert Shih","doi":"10.1115/1.4063521","DOIUrl":"https://doi.org/10.1115/1.4063521","url":null,"abstract":"Guest Editorial 2023 Manufacturing Science and Engineering Conference Accepted Manuscript Binil Starly, Binil Starly 5999 S Backus Mall Mesa, AZ 85212 Email: binil.starly@asu.edu Search for other works by this author on: This Site PubMed Google Scholar Albert Shih Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125 Email: shiha@umich.edu Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information Binil Starly 5999 S Backus Mall Mesa, AZ 85212 Albert Shih Mech Engrg 2350 Hayward St 1029 Dow Ann Arbor, MI 48109-2125 Email: shiha@umich.edu Email: binil.starly@asu.edu J. Manuf. Sci. Eng. 1-2 (2 pages) Paper No: MANU-23-1588 https://doi.org/10.1115/1.4063521 Published Online: September 27, 2023 Article history Received: September 22, 2023 Revised: September 23, 2023 Accepted: September 23, 2023 Published: September 27, 2023","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135945123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.
{"title":"A Spatio-Temporal Prediction and Planning Framework for Proactive Human-Robot Collaboration","authors":"Jared Flowers, Gloria Wiens","doi":"10.1115/1.4063502","DOIUrl":"https://doi.org/10.1115/1.4063502","url":null,"abstract":"Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"2 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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