The development of feasible methods for the design of power-skiving tools without cutting interference is essential in ensuring the accuracy of involute internal machined gears. One of the most crucial points in obtaining interference-free and re-sharpenable power-skiving tools is that of determining the cutting edge and clearance surface. The present study introduces a tilt angle during the power-skiving process to design a simple cylindrical interference-free tool shape, in which the shape of the cutting edge remains unchanged after re-sharpening. The relative position between the new tool center point and gear during machining is similarly unchanged after the re-sharpening process. In addition, the clearance angle between the tool and the gear can be easily adjusted simply by changing the tilt angle of the tool during power-skiving. The validity of the proposed design method is demonstrated through a simple numerical example. The simulation results confirm the feasibility of the proposed method.
{"title":"Algebraic modeling of cylindrical interference-free power-skiving tool for involute internal gear cutting with tilt angle","authors":"","doi":"10.1115/1.4062312","DOIUrl":"https://doi.org/10.1115/1.4062312","url":null,"abstract":"\u0000 The development of feasible methods for the design of power-skiving tools without cutting interference is essential in ensuring the accuracy of involute internal machined gears. One of the most crucial points in obtaining interference-free and re-sharpenable power-skiving tools is that of determining the cutting edge and clearance surface. The present study introduces a tilt angle during the power-skiving process to design a simple cylindrical interference-free tool shape, in which the shape of the cutting edge remains unchanged after re-sharpening. The relative position between the new tool center point and gear during machining is similarly unchanged after the re-sharpening process. In addition, the clearance angle between the tool and the gear can be easily adjusted simply by changing the tilt angle of the tool during power-skiving. The validity of the proposed design method is demonstrated through a simple numerical example. The simulation results confirm the feasibility of the proposed method.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47793554","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 Fiber-reinforced polymer (FRP) additive manufacturing has transformed fused filament fabrication (FFF) by manufacturing products with excellent mechanical characteristics. However, the surface finish and dimensional characteristics of printed FRP parts are typically poor due to protruding fibers and the stair-stepping effect. This parametric study examined an in-process combined mechanical plus chemical finishing technique to improve the surface finish of FRPs manufactured through FFF. This process is particularly useful for internal or complex features that cannot be otherwise finished after printing. In this work, a custom-built three-axis machine with printing, machining, and chemical finishing capabilities was used for the experiments. The effect of mechanical finishing on surface characteristics was first quantified using chip load and spindle speed as independent parameters. Following that, chemical treatment was performed on the already machined surface at two pressing depths (PD), which control the normal contact force acting on the surface. The best surface characteristics were observed at a low chip load of 0.007 mm and a moderately high spindle speed of 20,000 rpm. After chemical treatment using a lower PD, a surface roughness reduction was observed (from 8.041 to 4.988 µm). Increased PD led to even lower Ra values (from 4.988 to 3.538 µm) due to the enhanced fiber encapsulation phenomenon. Finally, the dimensional analysis revealed that the final combined finished samples had less than 1%-dimensional error (0.05 mm), which is an order of magnitude less than the typical error in FFF-printed parts (0.5 mm). This study provides means to conduct finishing in an additive manufacturing environment to reduce the time, labor, and cost associated with post-processing.
{"title":"Surface Characterization of Three-Dimensional Printed Fiber-Reinforced Polymer Following an In-Process Mechanical–Chemical Finishing Method","authors":"Aman Nigam, Bruce L. Tai","doi":"10.1115/1.4062146","DOIUrl":"https://doi.org/10.1115/1.4062146","url":null,"abstract":"Abstract Fiber-reinforced polymer (FRP) additive manufacturing has transformed fused filament fabrication (FFF) by manufacturing products with excellent mechanical characteristics. However, the surface finish and dimensional characteristics of printed FRP parts are typically poor due to protruding fibers and the stair-stepping effect. This parametric study examined an in-process combined mechanical plus chemical finishing technique to improve the surface finish of FRPs manufactured through FFF. This process is particularly useful for internal or complex features that cannot be otherwise finished after printing. In this work, a custom-built three-axis machine with printing, machining, and chemical finishing capabilities was used for the experiments. The effect of mechanical finishing on surface characteristics was first quantified using chip load and spindle speed as independent parameters. Following that, chemical treatment was performed on the already machined surface at two pressing depths (PD), which control the normal contact force acting on the surface. The best surface characteristics were observed at a low chip load of 0.007 mm and a moderately high spindle speed of 20,000 rpm. After chemical treatment using a lower PD, a surface roughness reduction was observed (from 8.041 to 4.988 µm). Increased PD led to even lower Ra values (from 4.988 to 3.538 µm) due to the enhanced fiber encapsulation phenomenon. Finally, the dimensional analysis revealed that the final combined finished samples had less than 1%-dimensional error (0.05 mm), which is an order of magnitude less than the typical error in FFF-printed parts (0.5 mm). This study provides means to conduct finishing in an additive manufacturing environment to reduce the time, labor, and cost associated with post-processing.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134955097","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}
M. Ansari, Hemant Agiwal, D. Franke, M. Zinn, F. Pfefferkorn, S. Rudraraju
This study employs a high-fidelity numerical framework to determine the plastic material flow patterns and temperature distributions that lead to void formation during friction stir welding (FSW), and to relate the void morphologies to the underlying alloy material properties and process conditions. Three aluminum alloys, viz., 6061-T6, 7075-T6, and 5053-H18 were investigated under varying traverse speeds. The choice of aluminum alloys enables investigation of a wide range of thermal and mechanical properties. The numerical simulations were validated using experimental observations of void morphologies in these three alloys. Temperatures, plastic strain rates, and material flow patterns are considered. The key results from this study are: (1) The predicted stir zone and void morphology are in good agreement with the experimental observations, (2) The temperature and plastic strain-rate maps in the steady-state process conditions show a strong dependency on the alloy type and traverse speeds, (3) The material velocity contours provide a good insight into the material flow in the stir zone for the FSW process conditions that result in voids as well as those that do not result in voids. The numerical model and the ensuing parametric studies presented in this work provide a framework for understanding material flow under different process conditions in aluminum alloys, and potentially in other alloys. Furthermore, the utility of the numerical model for making quantitative predictions and investigating different process parameters to reduce void formation is demonstrated.
{"title":"Numerical investigation into the influence of alloy type and thermo-mechanics on void formation in Friction Stir Welding of Aluminium alloys","authors":"M. Ansari, Hemant Agiwal, D. Franke, M. Zinn, F. Pfefferkorn, S. Rudraraju","doi":"10.1115/1.4062270","DOIUrl":"https://doi.org/10.1115/1.4062270","url":null,"abstract":"\u0000 This study employs a high-fidelity numerical framework to determine the plastic material flow patterns and temperature distributions that lead to void formation during friction stir welding (FSW), and to relate the void morphologies to the underlying alloy material properties and process conditions. Three aluminum alloys, viz., 6061-T6, 7075-T6, and 5053-H18 were investigated under varying traverse speeds. The choice of aluminum alloys enables investigation of a wide range of thermal and mechanical properties. The numerical simulations were validated using experimental observations of void morphologies in these three alloys. Temperatures, plastic strain rates, and material flow patterns are considered. The key results from this study are: (1) The predicted stir zone and void morphology are in good agreement with the experimental observations, (2) The temperature and plastic strain-rate maps in the steady-state process conditions show a strong dependency on the alloy type and traverse speeds, (3) The material velocity contours provide a good insight into the material flow in the stir zone for the FSW process conditions that result in voids as well as those that do not result in voids. The numerical model and the ensuing parametric studies presented in this work provide a framework for understanding material flow under different process conditions in aluminum alloys, and potentially in other alloys. Furthermore, the utility of the numerical model for making quantitative predictions and investigating different process parameters to reduce void formation is demonstrated.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"68 11","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41277575","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}
In order to establish a high-fidelity mechanism model for investigating the molten pool behaviors during directed energy deposition (DED) process, a molten pool dynamics model combined with the discrete element method is developed in present study. The proposed model contains newly added particle sources to further intuitively reproduce the interaction between the discrete powder particles and the molten pool. Meanwhile, the effects of the nozzle structure, carrier gas and shielding gas on the feedstock feeding process are simulated in detail using the gas-powder flow model based on the multi-phase flow theory. The gas-powder flow model is used to provide the reasonable outlet velocities, focal distance and radius of the focal point for the particle sources in the molten pool dynamics model, which solves the difficulty that the motion state of the powder streams obtained by the molten pool dynamics simulation are hard to reproduce the actual situation. Besides, relevant experiments are conducted to verify the accuracy of the developed models. The predicted parameters of the powder streams are consistent with the experiment, and the deviations of the predicted molten pool dimensions are less than 10%. The heat and mass transfer phenomena inside the molten pool are also revealed. Furthermore, the maximum size of the spherical pore defects is predicted to be 18.6 µm, which is underestimated by 7% compared to the microscopic observation. Taken together, the developed numerical model could augment and improve the training samples for the machine learning modelling of DED process.
{"title":"Multi-physics investigations on the gas-powder flow and the molten pool dynamics during directed energy deposition process","authors":"Chenghong Duan, X. Cao, Xiangpeng Luo, Dazhi Shang, X. Hao","doi":"10.1115/1.4062259","DOIUrl":"https://doi.org/10.1115/1.4062259","url":null,"abstract":"\u0000 In order to establish a high-fidelity mechanism model for investigating the molten pool behaviors during directed energy deposition (DED) process, a molten pool dynamics model combined with the discrete element method is developed in present study. The proposed model contains newly added particle sources to further intuitively reproduce the interaction between the discrete powder particles and the molten pool. Meanwhile, the effects of the nozzle structure, carrier gas and shielding gas on the feedstock feeding process are simulated in detail using the gas-powder flow model based on the multi-phase flow theory. The gas-powder flow model is used to provide the reasonable outlet velocities, focal distance and radius of the focal point for the particle sources in the molten pool dynamics model, which solves the difficulty that the motion state of the powder streams obtained by the molten pool dynamics simulation are hard to reproduce the actual situation. Besides, relevant experiments are conducted to verify the accuracy of the developed models. The predicted parameters of the powder streams are consistent with the experiment, and the deviations of the predicted molten pool dimensions are less than 10%. The heat and mass transfer phenomena inside the molten pool are also revealed. Furthermore, the maximum size of the spherical pore defects is predicted to be 18.6 µm, which is underestimated by 7% compared to the microscopic observation. Taken together, the developed numerical model could augment and improve the training samples for the machine learning modelling of DED process.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41738532","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}
Suyog Ghungrad, Meysam Faegh, B. Gould, S. Wolff, Azadeh Haghighi
Physics-informed deep learning (PIDL) is one of the emerging topics in additive manufacturing (AM). However, the success of previous PIDL approaches is generally significantly dependent on the existence of massive datasets. As the data collection in AM is usually challenging, a novel Architecture-driven PIDL structure named APIDL based on the deep unfolding approach for limited data scenarios has been proposed in the current study for predicting thermal history in the laser powder bed fusion process. The connections in this machine learning architecture are inspired by iterative thermal model equations. In other words, each iteration of the thermal model is mapped to a layer of the neural network. The hyper-parameters of the APIDL model are tuned, and its performance is analyzed. The APIDL for 1000 points with 80:20 split ratio achieves a testing mean absolute percentage error (MAPE) of 2.8% and R2 value of 0.936. The APIDL is compared with the artificial neural network, extra trees regressor (ETR), support vector regressor, and long short-term memory algorithms. It was shown that the proposed APIDL model outperforms the others. The MAPE and R2 of APIDL are 55.7% lower and 15.6% higher than the ETR, which had the best performance among other pure ML models.
{"title":"Architecture-driven Physics-informed Deep Learning for Temperature Prediction in Laser Powder Bed Fusion Additive Manufacturing with Limited Data","authors":"Suyog Ghungrad, Meysam Faegh, B. Gould, S. Wolff, Azadeh Haghighi","doi":"10.1115/1.4062237","DOIUrl":"https://doi.org/10.1115/1.4062237","url":null,"abstract":"\u0000 Physics-informed deep learning (PIDL) is one of the emerging topics in additive manufacturing (AM). However, the success of previous PIDL approaches is generally significantly dependent on the existence of massive datasets. As the data collection in AM is usually challenging, a novel Architecture-driven PIDL structure named APIDL based on the deep unfolding approach for limited data scenarios has been proposed in the current study for predicting thermal history in the laser powder bed fusion process. The connections in this machine learning architecture are inspired by iterative thermal model equations. In other words, each iteration of the thermal model is mapped to a layer of the neural network. The hyper-parameters of the APIDL model are tuned, and its performance is analyzed. The APIDL for 1000 points with 80:20 split ratio achieves a testing mean absolute percentage error (MAPE) of 2.8% and R2 value of 0.936. The APIDL is compared with the artificial neural network, extra trees regressor (ETR), support vector regressor, and long short-term memory algorithms. It was shown that the proposed APIDL model outperforms the others. The MAPE and R2 of APIDL are 55.7% lower and 15.6% higher than the ETR, which had the best performance among other pure ML models.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42822968","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}
Seng Xiang, Xingtao Liu, Li-cong An, Haozheng J. Qu, G. Cheng
Modulating the heating and cooling during plastic deformation has been critical to control the microstructure and phase change in metals. During laser shock peening under optimal elevated temperatures, high-density dislocations and nanoprecipitates can be generated to greatly enhance material strength and fatigue life in metals. In this paper, we propose a general methodology to modulate the heating and cooling during laser shock processing via temporal pulse shaping, namely dual pulse laser shock peening (DP-LSP), which combines both ultrafast-heating and laser shock peening in one operation to generate desired microstructure and mechanical property. Single pulse LSP was able to remelt large second phase precipitates due to fast cooling, resulting in smaller grains (500nm), while using DP-LSP with appropriate pulse durations, dynamic precipitation effects can generate nanosized (30nm) intermetallic phase Al3Ti with high density. By generation of grain size refinement, high density nanoscale precipitates, and dislocations, the yield strength increase by 18% and 102% compared with single pulse processing, and original sample respectively. A phase-field model (PFM) and multiscale dislocation dynamics (MDD) were applied to study dislocation dynamics and nanoprecipitation generation during DP-LSP, and their interaction. The work provides a basis for controlling microstructure by DP-LSP to enhance mechanical properties in metals.
{"title":"Nanoengineered Laser Shock Processing via Pulse Shaping for Nanostructuring in Metals: Multiscale simulations and experiments","authors":"Seng Xiang, Xingtao Liu, Li-cong An, Haozheng J. Qu, G. Cheng","doi":"10.1115/1.4062234","DOIUrl":"https://doi.org/10.1115/1.4062234","url":null,"abstract":"\u0000 Modulating the heating and cooling during plastic deformation has been critical to control the microstructure and phase change in metals. During laser shock peening under optimal elevated temperatures, high-density dislocations and nanoprecipitates can be generated to greatly enhance material strength and fatigue life in metals. In this paper, we propose a general methodology to modulate the heating and cooling during laser shock processing via temporal pulse shaping, namely dual pulse laser shock peening (DP-LSP), which combines both ultrafast-heating and laser shock peening in one operation to generate desired microstructure and mechanical property. Single pulse LSP was able to remelt large second phase precipitates due to fast cooling, resulting in smaller grains (500nm), while using DP-LSP with appropriate pulse durations, dynamic precipitation effects can generate nanosized (30nm) intermetallic phase Al3Ti with high density. By generation of grain size refinement, high density nanoscale precipitates, and dislocations, the yield strength increase by 18% and 102% compared with single pulse processing, and original sample respectively. A phase-field model (PFM) and multiscale dislocation dynamics (MDD) were applied to study dislocation dynamics and nanoprecipitation generation during DP-LSP, and their interaction. The work provides a basis for controlling microstructure by DP-LSP to enhance mechanical properties in metals.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48496380","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}
With the wide application of new electric vehicle (EV) battery in various industrial fields, it is important to establish a systematic intelligent battery recycling system that can be used to find out the resource wastes and environmental impacts for the retired EV battery. By combining the disassembly and echelon utilization of EV battery recycling in the re-manufacturing fields, human-robot collaboration (HRC) disassembly method can be used to solve many huge challenges about the efficiency and safety of retired EV battery recycling. In order to find out the common problems in the human-robot collaboration disassembly process of EV battery recycling, a dynamic disassembly process optimization method based on Multi-Agent Reinforcement Learning (MARL) algorithm is proposed. Furthermore, it is necessary to disassemble the EV battery disassembly task trajectory based on human-robot collaboration disassembly task in 2D planar, which can be used to acquire the optimal disassembly paths in the same disassembly planar combining the Q-learning algorithm. The disassembly task sequence can be completed through standard trajectory matching. Finally the feasibility of the method is verified by disassembly operations for a specific battery module case.
{"title":"Multi-agent Reinforcement Learning Method for Disassembly Sequential Task Optimization Based on Human-Robot Collaborative Disassembly in Electric Vehicle Battery Recycling","authors":"Jinhua Xiao, Jiaxu Gao, N. Anwer, B. Eynard","doi":"10.1115/1.4062235","DOIUrl":"https://doi.org/10.1115/1.4062235","url":null,"abstract":"\u0000 With the wide application of new electric vehicle (EV) battery in various industrial fields, it is important to establish a systematic intelligent battery recycling system that can be used to find out the resource wastes and environmental impacts for the retired EV battery. By combining the disassembly and echelon utilization of EV battery recycling in the re-manufacturing fields, human-robot collaboration (HRC) disassembly method can be used to solve many huge challenges about the efficiency and safety of retired EV battery recycling. In order to find out the common problems in the human-robot collaboration disassembly process of EV battery recycling, a dynamic disassembly process optimization method based on Multi-Agent Reinforcement Learning (MARL) algorithm is proposed. Furthermore, it is necessary to disassemble the EV battery disassembly task trajectory based on human-robot collaboration disassembly task in 2D planar, which can be used to acquire the optimal disassembly paths in the same disassembly planar combining the Q-learning algorithm. The disassembly task sequence can be completed through standard trajectory matching. Finally the feasibility of the method is verified by disassembly operations for a specific battery module case.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44396908","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}
Digital maskless lithography is growing in popularity due to its unique ability to fabricate high-resolution parts at a fast speed without the need for physical masks. Though the theoretical foundation for photopolymerization exists, it is difficult to observe the voxel growth process in situ. This can be attributed to the low refractive index difference between cured and uncured resin, the microscopic size of the parts, and the rapid rate of photopolymerization after crossing the threshold. Therefore, a system that can address these issues is highly desired. Schlieren optics is a tool that makes the minute changes in the refractive indices visible. This paper proposes a modified schlieren-based observation system with confocal magnifying optics that create a virtual screen at the focal plane of the camera. The proposed technique visualizes the light deflection by the changing density induced refractive index gradient, and the use of focusing optics enables flexible positioning of the virtual screen and optical magnification. Single-shot binary images with a different number of pixels were used for fabricating voxels. Different factors affecting the voxel shape like chemical composition, energy input is studied. The observed results are compared against simulations based on Beer-Lambert's law, photopolymerization curve, and Gaussian beam propagation theory. The physical experimental results demonstrated the effectiveness of the proposed observation system. Application of this system in fabrication of microlenses and its advantages over theoretical model-based profile predictions are briefly discussed.
{"title":"Enhanced Schlieren System for In-Situ Observation of Dynamic Light-Resin Interactions in Projection-based Stereolithography Process","authors":"Aditya Chivate, Chi Zhou","doi":"10.1115/1.4062218","DOIUrl":"https://doi.org/10.1115/1.4062218","url":null,"abstract":"\u0000 Digital maskless lithography is growing in popularity due to its unique ability to fabricate high-resolution parts at a fast speed without the need for physical masks. Though the theoretical foundation for photopolymerization exists, it is difficult to observe the voxel growth process in situ. This can be attributed to the low refractive index difference between cured and uncured resin, the microscopic size of the parts, and the rapid rate of photopolymerization after crossing the threshold. Therefore, a system that can address these issues is highly desired. Schlieren optics is a tool that makes the minute changes in the refractive indices visible. This paper proposes a modified schlieren-based observation system with confocal magnifying optics that create a virtual screen at the focal plane of the camera. The proposed technique visualizes the light deflection by the changing density induced refractive index gradient, and the use of focusing optics enables flexible positioning of the virtual screen and optical magnification. Single-shot binary images with a different number of pixels were used for fabricating voxels. Different factors affecting the voxel shape like chemical composition, energy input is studied. The observed results are compared against simulations based on Beer-Lambert's law, photopolymerization curve, and Gaussian beam propagation theory. The physical experimental results demonstrated the effectiveness of the proposed observation system. Application of this system in fabrication of microlenses and its advantages over theoretical model-based profile predictions are briefly discussed.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48286766","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}
Yaoyao Ping, Yongkui Liu, Lin Zhang, Lihui Wang, Xun Xu
Cloud manufacturing is a manufacturing model that aims to provide on-demand resources and services to consumers over the Internet. Scheduling is one of the core techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is an important research issue in the area of cloud manufacturing scheduling. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to solve the issue, which, however, are either incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) that combines artificial neural networks with reinforcement learning provides an effective technique in this regard. In view of this, we employ a typical deep reinforcement learning algorithm – Deep Q-network (DQN) – and proposed a DQN-based multi-task scheduling approach for cloud manufacturing. Three different task arrival modes – arriving at the same time, arriving in random batches, and arriving one by one sequentially – are considered. Four baseline approaches including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is able to effectively address the multi-task scheduling problem in cloud manufacturing and performs best among all approaches.
{"title":"Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing under Different Task Arrival Modes","authors":"Yaoyao Ping, Yongkui Liu, Lin Zhang, Lihui Wang, Xun Xu","doi":"10.1115/1.4062217","DOIUrl":"https://doi.org/10.1115/1.4062217","url":null,"abstract":"\u0000 Cloud manufacturing is a manufacturing model that aims to provide on-demand resources and services to consumers over the Internet. Scheduling is one of the core techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is an important research issue in the area of cloud manufacturing scheduling. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to solve the issue, which, however, are either incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) that combines artificial neural networks with reinforcement learning provides an effective technique in this regard. In view of this, we employ a typical deep reinforcement learning algorithm – Deep Q-network (DQN) – and proposed a DQN-based multi-task scheduling approach for cloud manufacturing. Three different task arrival modes – arriving at the same time, arriving in random batches, and arriving one by one sequentially – are considered. Four baseline approaches including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is able to effectively address the multi-task scheduling problem in cloud manufacturing and performs best among all approaches.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45880001","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}
In this work, we developed a new additive manufacturing paradigm, coaxial wire-powder fed directed energy deposition (CWP-DED), to enable the fabrication of metals or composites with high manufacturing flexibility and efficiency. Herein, stainless steel (SS) 316L was selected as a representative material to validate the feasibility of CWP-DED process. Effects of feed rates on the melt pool thermodynamics during the CWP-DED process were investigated using experimental and analytical approaches. Thermal contributions of fed wire and powders to the melt pool were involved in the analytical model to predict the melt pool temperature. The experimental results from thermal imaging were also obtained for validation. Besides, we uncovered the evolution of solidification morphology and crystallographic texture with different combinations of wire and powder feed rates. Finally, the microhardness and tensile performance of different as-built parts were tested. The results showed that the powder feed rate played a more dominant role in determining the melt pool temperature than the wire feed rate. Melt pool temperature experienced an initial increase and then decrease with the powder feed rate. A fine microstructure was achieved at a low powder feed rate, producing higher microhardness and larger tensile strength. This paper revealed the relations among process, thermal variation, microstructures, and mechanical properties of as-built metallic parts to provide a fundamental understanding of this novel DED process.
{"title":"Directed Energy Deposition with Coaxial Wire-Powder Feeding: Melt Pool Temperature and Microstructure","authors":"Yue Zhou, F. Ning","doi":"10.1115/1.4062216","DOIUrl":"https://doi.org/10.1115/1.4062216","url":null,"abstract":"\u0000 In this work, we developed a new additive manufacturing paradigm, coaxial wire-powder fed directed energy deposition (CWP-DED), to enable the fabrication of metals or composites with high manufacturing flexibility and efficiency. Herein, stainless steel (SS) 316L was selected as a representative material to validate the feasibility of CWP-DED process. Effects of feed rates on the melt pool thermodynamics during the CWP-DED process were investigated using experimental and analytical approaches. Thermal contributions of fed wire and powders to the melt pool were involved in the analytical model to predict the melt pool temperature. The experimental results from thermal imaging were also obtained for validation. Besides, we uncovered the evolution of solidification morphology and crystallographic texture with different combinations of wire and powder feed rates. Finally, the microhardness and tensile performance of different as-built parts were tested. The results showed that the powder feed rate played a more dominant role in determining the melt pool temperature than the wire feed rate. Melt pool temperature experienced an initial increase and then decrease with the powder feed rate. A fine microstructure was achieved at a low powder feed rate, producing higher microhardness and larger tensile strength. This paper revealed the relations among process, thermal variation, microstructures, and mechanical properties of as-built metallic parts to provide a fundamental understanding of this novel DED process.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"60 3","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41315320","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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