Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.082
Eyob Messele Sefene , Steve Hsueh-Ming Wang , Chao-Chang Arthur Chen
Diamond wire sawing (DWS) is a primary and fundamental stage for slicing large-diameter ingots into multiple wafers, enabling high-volume production in a single process. However, the extended contact length between the diamond wire and work material generates heat, which detrimentally impacts the surface quality of the sliced wafers and accelerates the diamond wire wear rate. This study implemented a rocking mode sawing strategy to investigate the effect of contact length on the surface quality of as-sawn wafer and diamond wire wear rate. Experiments have been conducted on monocrystalline silicon carbide (4H-SiC) with and without a rocking-mode multi-DWS machine. The experimental sawing temperature has been validated using Fourier’s law of thermal conduction, a finite element model, and a linear time series regression model. Results indicated that the minimal sawing temperature had been observed with the rocking mode sawing strategy, attributed to its shorter contact length compared to the process without rocking mode. Additionally, the finite element and regression models closely matched the experimental data, achieving accuracies of 93.57 % and 99.96 %, respectively. Fourier’s law of thermal conduction proved significant for precisely determining the sawing temperature. Notably, the rocking mode sawing strategy significantly affected the sawing temperature, surface quality, and diamond wire wear rate compared with the sawing process without the rocking mode.
{"title":"Analysis of contact length and temperature effect in rocking mode diamond wire sawing of monocrystalline silicon carbide wafer","authors":"Eyob Messele Sefene , Steve Hsueh-Ming Wang , Chao-Chang Arthur Chen","doi":"10.1016/j.mfglet.2024.09.082","DOIUrl":"10.1016/j.mfglet.2024.09.082","url":null,"abstract":"<div><div>Diamond wire sawing (DWS) is a primary and fundamental stage for slicing large-diameter ingots into multiple wafers, enabling high-volume production in a single process. However, the extended contact length between the diamond wire and work material generates heat, which detrimentally impacts the surface quality of the sliced wafers and accelerates the diamond wire wear rate. This study implemented a rocking mode sawing strategy to investigate the effect of contact length on the surface quality of as-sawn wafer and diamond wire wear rate. Experiments have been conducted on monocrystalline silicon carbide (4H-SiC) with and without a rocking-mode multi-DWS machine. The experimental sawing temperature has been validated using Fourier’s law of thermal conduction, a finite element model, and a linear time series regression model. Results indicated that the minimal sawing temperature had been observed with the rocking mode sawing strategy, attributed to its shorter contact length compared to the process without rocking mode. Additionally, the finite element and regression models closely matched the experimental data, achieving accuracies of 93.57 % and 99.96 %, respectively. Fourier’s law of thermal conduction proved significant for precisely determining the sawing temperature. Notably, the rocking mode sawing strategy significantly affected the sawing temperature, surface quality, and diamond wire wear rate compared with the sawing process without the rocking mode.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 641-652"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.094
Asif Rashid, Akshar Kota, Shreyes N. Melkote
Wire-Arc Directed Energy Deposition (Wire-Arc DED) has emerged as a promising additive manufacturing technique known for its high deposition rates. However, the variability in microstructure and mechanical properties (e.g., hardness) of the manufactured components poses significant challenges. This study delves into these issues, focusing on the influence of interlayer machining on the microstructural evolution and mechanical properties of thin-wall Wire-Arc DED structures. It is shown that as-built Wire-Arc DED structures exhibit a pronounced microstructure variation between different regions along the build direction, primarily governed by the differences in thermal history. In contrast, a Hybrid Wire-Arc DED process that integrates interlayer machining into the build process to induce severe plastic deformation leads to a microstructure characterized by refinement and homogenization, compared to a Wire-Arc DED process. This study provides insights into the impacts of plastic deformation due to machining and thermal cycling due to subsequent layer depositions on the microstructure and hardness obtained in Wire-Arc DED and Hybrid Wire-Arc DED processes, highlighting the potential of hybrid manufacturing to generate tailored microstructures to enhance the mechanical performance of functional components.
{"title":"Evolution of microstructure and mechanical property enhancement in wire-arc directed energy deposition with interlayer machining","authors":"Asif Rashid, Akshar Kota, Shreyes N. Melkote","doi":"10.1016/j.mfglet.2024.09.094","DOIUrl":"10.1016/j.mfglet.2024.09.094","url":null,"abstract":"<div><div>Wire-Arc Directed Energy Deposition (Wire-Arc DED) has emerged as a promising additive manufacturing technique known for its high deposition rates. However, the variability in microstructure and mechanical properties (e.g., hardness) of the manufactured components poses significant challenges. This study delves into these issues, focusing on the influence of interlayer machining on the microstructural evolution and mechanical properties of thin-wall Wire-Arc DED structures. It is shown that as-built Wire-Arc DED structures exhibit a pronounced microstructure variation between different regions along the build direction, primarily governed by the differences in thermal history. In contrast, a Hybrid Wire-Arc DED process that integrates interlayer machining into the build process to induce severe plastic deformation leads to a microstructure characterized by refinement and homogenization, compared to a Wire-Arc DED process. This study provides insights into the impacts of plastic deformation due to machining and thermal cycling due to subsequent layer depositions on the microstructure and hardness obtained in Wire-Arc DED and Hybrid Wire-Arc DED processes, highlighting the potential of hybrid manufacturing to generate tailored microstructures to enhance the mechanical performance of functional components.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 758-765"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.058
Enrico Simonetto, Ali Beigzadeh, Andrea Ghiotti, Stefania Bruschi
In recent years, with the emergence of Industry 4.0 trends and the impact of the COVID-19 pandemic, the agile manufacturing paradigm has gained increasing significance. Substantial efforts have been directed towards introducing new methods, driven by the dual objectives of flexibility and agility. This paper presents an innovative machine that employs two rollers to apply localized deformation to sheet metal through repetitive movement along the length of the sheet. As with all incremental forming processes, the forming strategy is a critical parameter influencing both the forming force and the dimensional accuracy of the manufactured workpiece. In this research, two different forming strategies, internal and external, were implemented for manufacturing elongated 90 deg bends on 1 mm and 3 mm thick AISI 304 sheets. Both numerical and experimental analyses were performed to assess the effects of these different strategies. The results confirm that the forming force with the external strategy is 50 % and 47 % less for the 1 mm and 3 mm sheets, respectively. Moreover, the external forming strategy allows for better control of the obtained angle, but limits the minimum obtainable bending radius.
{"title":"Evaluation of the effect of forming strategy in newly introduced flexible roll forming process","authors":"Enrico Simonetto, Ali Beigzadeh, Andrea Ghiotti, Stefania Bruschi","doi":"10.1016/j.mfglet.2024.09.058","DOIUrl":"10.1016/j.mfglet.2024.09.058","url":null,"abstract":"<div><div>In recent years, with the emergence of Industry 4.0 trends and the impact of the COVID-19 pandemic, the agile manufacturing paradigm has gained increasing significance. Substantial efforts have been directed towards introducing new methods, driven by the dual objectives of flexibility and agility. This paper presents an innovative machine that employs two rollers to apply localized deformation to sheet metal through repetitive movement along the length of the sheet. As with all incremental forming processes, the forming strategy is a critical parameter influencing both the forming force and the dimensional accuracy of the manufactured workpiece. In this research, two different forming strategies, internal and external, were implemented for manufacturing elongated 90 deg bends on 1 mm and 3 mm thick AISI 304 sheets. Both numerical and experimental analyses were performed to assess the effects of these different strategies. The results confirm that the forming force with the external strategy is 50 % and 47 % less for the 1 mm and 3 mm sheets, respectively. Moreover, the external forming strategy allows for better control of the obtained angle, but limits the minimum obtainable bending radius.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 475-482"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An English wheel is an exceedingly adaptable instrument in traditional metalworking. It is a manual manufacturing technique, enabling skilled craftsmen and blacksmiths to shape complex compound curves in sheet metal panels. Accurate measurements and precise adjustments are essential when operating an English wheel to ensure that the metal is shaped with the desired curvature. An automated method to form English wheeled panels through robot forming has recently been proposed. For such a method to be successful, accurate tracking of sheet information including positions, orientations, and deformation is important for error compensation and the design of the subsequent tool paths. In this study, a Vicon motion capture system is employed to monitor the position and shape of the sheet metal during the English wheeling process. The initial experimental results demonstrate the potential of such an in-process metrology system, along with possible avenues for future work.
{"title":"In-process part tracking and shape measurement using vision-based motion capture for automated English wheeling","authors":"Yahui Zhang, Derick Suarez, Kornel Ehmann, Jian Cao, Ping Guo","doi":"10.1016/j.mfglet.2024.09.028","DOIUrl":"10.1016/j.mfglet.2024.09.028","url":null,"abstract":"<div><div>An English wheel is an exceedingly adaptable instrument in traditional metalworking. It is a manual manufacturing technique, enabling skilled craftsmen and blacksmiths to shape complex compound curves in sheet metal panels. Accurate measurements and precise adjustments are essential when operating an English wheel to ensure that the metal is shaped with the desired curvature. An automated method to form English wheeled panels through robot forming has recently been proposed. For such a method to be successful, accurate tracking of sheet information including positions, orientations, and deformation is important for error compensation and the design of the subsequent tool paths. In this study, a Vicon motion capture system is employed to monitor the position and shape of the sheet metal during the English wheeling process. The initial experimental results demonstrate the potential of such an in-process metrology system, along with possible avenues for future work.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 241-247"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.064
Ankit Varma , Kewei Li , Laine Mears , Hongseok Choi , Xin Zhao
Dissimilar material joining is essential for improving the strength-to-weight ratio of materials for various applications. Friction element welding (FEW) is a promising solution for joining highly dissimilar materials that vary in strength and thickness. However, the influence of the process parameters on the material’s resultant microstructure and mechanical properties remains unclear. In this study, the relationship between microstructure and microhardness distribution of the welded specimen is experimentally studied, and the effects of temperature and stress evolution are revealed by a thermal–mechanical finite element model. It is found that the microhardness can be improved by over 50% in the central region due to microstructural change and grain refinement. The beneficial microstructural change can be achieved by inducing either a high peak temperature (over the austenitization temperature) or a high peak stress (over the hardening factor) during the FEW process, which can be obtained by controlling the endload and rotational speed of the friction element. The size of the region with improved hardness is observed to vary with the depth of deformation in the steel layer. For the transverse shear strength (TSS), it is observed that irrespective of the temperature levels reached, TSS increases with increasing stress in the steel layer. Temperature plays a crucial role when the steel layer’s temperature is higher than the austenitization start temperature wherein TSS increases with the temperature.
{"title":"Effects of temperature and stress evolution on microstructural change and mechanical properties during friction element welding","authors":"Ankit Varma , Kewei Li , Laine Mears , Hongseok Choi , Xin Zhao","doi":"10.1016/j.mfglet.2024.09.064","DOIUrl":"10.1016/j.mfglet.2024.09.064","url":null,"abstract":"<div><div>Dissimilar material joining is essential for improving the strength-to-weight ratio of materials for various applications. Friction element welding (FEW) is a promising solution for joining highly dissimilar materials that vary in strength and thickness. However, the influence of the process parameters on the material’s resultant microstructure and mechanical properties remains unclear. In this study, the relationship between microstructure and microhardness distribution of the welded specimen is experimentally studied, and the effects of temperature and stress evolution are revealed by a thermal–mechanical finite element model. It is found that the microhardness can be improved by over 50% in the central region due to microstructural change and grain refinement. The beneficial microstructural change can be achieved by inducing either a high peak temperature (over the austenitization temperature) or a high peak stress (over the hardening factor) during the FEW process, which can be obtained by controlling the endload and rotational speed of the friction element. The size of the region with improved hardness is observed to vary with the depth of deformation in the steel layer. For the transverse shear strength (TSS), it is observed that irrespective of the temperature levels reached, TSS increases with increasing stress in the steel layer. Temperature plays a crucial role when the steel layer’s temperature is higher than the austenitization start temperature wherein TSS increases with the temperature.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 526-535"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.026
Dawei Xue , Xi Gu , Hae Chang Gea
Production overrun is a common practice in manufacturing to meet the demand by increasing the number of qualified products and compensating for manufacturing defects. While production overrun can improve Supply Chain Network Reliability (SCNR), it leads to higher material costs. In this paper, a model is proposed to evaluate SCNR by incorporating the reliabilities of inbound logistics, operations, and outbound logistics. Based on the proposed SCNR model, we study the optimal production overrun of each manufacturing site in a supply chain network and identify the production plan that satisfies the reliability requirement with minimum total production overrun penalty. By analyzing the monotonicity of the objective and constraint functions of the formulated problem, an algorithm based on linearization is developed to solve this optimization problem. Numerical examples across various scales are presented to illustrate the developed model and method. The impact of the penalty coefficient of the production overrun on the result is investigated. The results from the numerical examples provide managerial insights on allocating resources in the entire supply chain network and improving the supply chain reliability and competitiveness.
{"title":"Production overrun optimization considering supply chain network reliability","authors":"Dawei Xue , Xi Gu , Hae Chang Gea","doi":"10.1016/j.mfglet.2024.09.026","DOIUrl":"10.1016/j.mfglet.2024.09.026","url":null,"abstract":"<div><div>Production overrun is a common practice in manufacturing to meet the demand by increasing the number of qualified products and compensating for manufacturing defects. While production overrun can improve Supply Chain Network Reliability (SCNR), it leads to higher material costs. In this paper, a model is proposed to evaluate SCNR by incorporating the reliabilities of inbound logistics, operations, and outbound logistics. Based on the proposed SCNR model, we study the optimal production overrun of each manufacturing site in a supply chain network and identify the production plan that satisfies the reliability requirement with minimum total production overrun penalty. By analyzing the monotonicity of the objective and constraint functions of the formulated problem, an algorithm based on linearization is developed to solve this optimization problem. Numerical examples across various scales are presented to illustrate the developed model and method. The impact of the penalty coefficient of the production overrun on the result is investigated. The results from the numerical examples provide managerial insights on allocating resources in the entire supply chain network and improving the supply chain reliability and competitiveness.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 219-228"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.021
Asmaa Harfoush , Zhaoyan Fan , Lisbeth Goddik , Karl R. Haapala
The food industry faces several challenges, including intricate supply chains, compliance with food safety regulations, sustainability concerns, and the rising demand for high-quality products. Furthermore, consumers increasingly seek personalized food products with specific fat, sugar, and micronutrient levels. The ice cream industry is no exception in facing these challenges. Fortunately, Industry 4.0 technologies, such as smart manufacturing, data analytics, and the Industrial Internet of Things (IIoT), offer viable solutions to many of the aforementioned challenges. However, a deeper understanding of industrial ice cream manufacturing processes and systems is essential to apply these technologies effectively. While the related literature has often focused on ingredient selection to achieve the desired ice cream flavor and texture, there is a noticeable absence of comprehensive efforts to evaluate the impact of process- and systems-related aspects in ice cream manufacturing. This study employs a semi-systematic literature review approach to compile recent research that examines the influence of process- and system-level factors on ice cream product quality and production processes, focusing on the aspects that can benefit from implementing Industry 4.0 technologies. The literature review reveals that 1) at the process level, researchers have focused on three key processes (i.e., pasteurization, homogenization, and dynamic freezing) and their impact on the quality of the ice cream; 2) at the system level, researchers have concentrated their efforts on techno-economic factors, process scheduling, productivity, and sustainability.
{"title":"A review of ice cream manufacturing process and system improvement strategies","authors":"Asmaa Harfoush , Zhaoyan Fan , Lisbeth Goddik , Karl R. Haapala","doi":"10.1016/j.mfglet.2024.09.021","DOIUrl":"10.1016/j.mfglet.2024.09.021","url":null,"abstract":"<div><div>The food industry faces several challenges, including intricate supply chains, compliance with food safety regulations, sustainability concerns, and the rising demand for high-quality products. Furthermore, consumers increasingly seek personalized food products with specific fat, sugar, and micronutrient levels. The ice cream industry is no exception in facing these challenges. Fortunately, Industry 4.0 technologies, such as smart manufacturing, data analytics, and the Industrial Internet of Things (IIoT), offer viable solutions to many of the aforementioned challenges. However, a deeper understanding of industrial ice cream manufacturing processes and systems is essential to apply these technologies effectively. While the related literature has often focused on ingredient selection to achieve the desired ice cream flavor and texture, there is a noticeable absence of comprehensive efforts to evaluate the impact of process- and systems-related aspects in ice cream manufacturing. This study employs a semi-systematic literature review approach to compile recent research that examines the influence of process- and system-level factors on ice cream product quality and production processes, focusing on the aspects that can benefit from implementing Industry 4.0 technologies. The literature review reveals that 1) at the process level, researchers have focused on three key processes (i.e., pasteurization, homogenization, and dynamic freezing) and their impact on the quality of the ice cream; 2) at the system level, researchers have concentrated their efforts on techno-economic factors, process scheduling, productivity, and sustainability.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 170-181"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.005
Steven Rice , Ahmed Azab , Sherif Saad
Inverse kinematics is a core aspect of robot manipulation. This paper presents an approach to solving Inverse Kinematics (IK) for robots, including articulated industrial ones, combining deep learning with an evolutionary algorithm. Fusion IK passes the manipulator’s target and current joint values into a neural network, the results of which are then used to seed an evolutionary algorithm, Bio IK, to complete the solution of the inverse kinematics problem. Fusion IK allows for solving the position and orientation of the robot while attempting to minimize joint movement times. Comparisons between Fusion IK and its underlying algorithm Bio IK are tested on a six-degree-of-freedom articulated industrial robot as well as a 20-degree-of-freedom robot to explore the move times that Fusion IK produces. The comparisons show that the variations of the Fusion IK algorithm show comparable results to its underlying evolutionary Bio IK algorithm on a six-degrees-of-freedom articulated robot and improvements on a 20-degree-of-freedom robot without any additional hyperparameter tuning. The results show that Fusion IK could be of real value regarding the movement time and the quality of the obtained solutions upon further research, especially with higher degree-of-freedom robots.
逆运动学是机器人操纵的一个核心方面。本文介绍了一种结合深度学习与进化算法的机器人逆运动学（IK）求解方法，包括关节型工业机器人。Fusion IK 将机械手的目标值和当前关节值传入神经网络，然后利用神经网络的结果为进化算法 Bio IK 提供种子，完成逆运动学问题的求解。Fusion IK 可以解决机器人的位置和方向问题，同时试图最大限度地减少关节运动时间。我们在一个六自由度铰接式工业机器人和一个 20 自由度机器人上测试了 Fusion IK 与其基础算法 Bio IK 之间的比较，以探索 Fusion IK 所产生的移动时间。比较结果表明，在六自由度铰接式机器人上，Fusion IK 算法的变体与其底层进化 Bio IK 算法的结果相当，而在 20 自由度机器人上则有所改进，无需额外调整超参数。研究结果表明，Fusion IK 在运动时间和所获解决方案质量方面具有实际价值，有待进一步研究，特别是在更高自由度的机器人上。
{"title":"Fusion IK: Solving inverse kinematics using a hybridized deep learning and evolutionary approach","authors":"Steven Rice , Ahmed Azab , Sherif Saad","doi":"10.1016/j.mfglet.2024.09.005","DOIUrl":"10.1016/j.mfglet.2024.09.005","url":null,"abstract":"<div><div>Inverse kinematics is a core aspect of robot manipulation. This paper presents an approach to solving Inverse Kinematics (IK) for robots, including articulated industrial ones, combining deep learning with an evolutionary algorithm. Fusion IK passes the manipulator’s target and current joint values into a neural network, the results of which are then used to seed an evolutionary algorithm, Bio IK, to complete the solution of the inverse kinematics problem. Fusion IK allows for solving the position and orientation of the robot while attempting to minimize joint movement times. Comparisons between Fusion IK and its underlying algorithm Bio IK are tested on a six-degree-of-freedom articulated industrial robot as well as a 20-degree-of-freedom robot to explore the move times that Fusion IK produces. The comparisons show that the variations of the Fusion IK algorithm show comparable results to its underlying evolutionary Bio IK algorithm on a six-degrees-of-freedom articulated robot and improvements on a 20-degree-of-freedom robot without any additional hyperparameter tuning. The results show that Fusion IK could be of real value regarding the movement time and the quality of the obtained solutions upon further research, especially with higher degree-of-freedom robots.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 9-18"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.007
Yukun Xiao , Guangyan Ge , Ming Deng , Jun Lv , Zhengchun Du
Efficient and accurate measurement and identification of geometric errors are crucial for improving the precision of CNC machine tools. The X-AX Laserbar, as a novel tool for indirect measurement, has not been extensively studied for the identification of geometric errors in machine tools. In this paper, the geometric error model for a three-axis machine tool is established to illustrate the multilateration measurement principle of the laserbar, and a non-redundant and unconstrained identification method is proposed to identify these geometric errors. This method avoids the use of redundant parameters and additional constraints by employing pose error twists to describe the geometric errors. These pose error twists are identified in a transitional coordinate system, and then the geometric errors will be identified in the machine coordinate system by deriving the relationship between the pose errors and geometric errors. The proposed method is validated with the VMC-850E three-axis machine tool. The geometric error measurement using a laserbar is completed in about 40 min, showing great efficiency. The experimental results indicate that the proposed method is capable of accurately identifying the 17 geometric errors required for error compensation. The identified geometric errors are then applied to the machine tool’s accuracy improvement through error compensation. The results show that the actual geometric errors are controlled to a low level. The proposed method can efficiently measure the geometric errors of three-axis machine tools and contribute significantly to improving their geometric accuracy.
{"title":"An unconstrained and non-redundant identification method of geometric errors and compensation of machine tools by X-AX Laserbar","authors":"Yukun Xiao , Guangyan Ge , Ming Deng , Jun Lv , Zhengchun Du","doi":"10.1016/j.mfglet.2024.09.007","DOIUrl":"10.1016/j.mfglet.2024.09.007","url":null,"abstract":"<div><div>Efficient and accurate measurement and identification of geometric errors are crucial for improving the precision of CNC machine tools. The X-AX Laserbar, as a novel tool for indirect measurement, has not been extensively studied for the identification of geometric errors in machine tools. In this paper, the geometric error model for a three-axis machine tool is established to illustrate the multilateration measurement principle of the laserbar, and a non-redundant and unconstrained identification method is proposed to identify these geometric errors. This method avoids the use of redundant parameters and additional constraints by employing pose error twists to describe the geometric errors. These pose error twists are identified in a transitional coordinate system, and then the geometric errors will be identified in the machine coordinate system by deriving the relationship between the pose errors and geometric errors. The proposed method is validated with the VMC-850E three-axis machine tool. The geometric error measurement using a laserbar is completed in about 40 min, showing great efficiency. The experimental results indicate that the proposed method is capable of accurately identifying the 17 geometric errors required for error compensation. The identified geometric errors are then applied to the machine tool’s accuracy improvement through error compensation. The results show that the actual geometric errors are controlled to a low level. The proposed method can efficiently measure the geometric errors of three-axis machine tools and contribute significantly to improving their geometric accuracy.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 31-42"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.076
Jun-Young Oh, Jae-Eun Kim, Wonkyun Lee
In recent developments in the field of manufacturing systems, there has been a growing emphasis on optimizing cutting conditions. These optimizations are primarily based on intricate parameters, such as the material removal rate (MRR), surface roughness, and position accuracy. Simultaneously, there’s an increasing focus on enhancing manufacturing efficiency through equipment maintenance strategies that consider parameters, such as corrosion, pressure, temperature, vibration, and other environmental factors. Wear is inevitable during processing, which affects productivity. It is generated in various forms, such as flank, crater, and edge wear, which reduce the tool lifespan and impact machining quality, especially by increasing the cutting forces. Various studies have been conducted to address this issue. Direct measurements using microscopes have high accuracy but require interruption during the process, which adversely affects efficiency and productivity. As a solution, the modern era has witnessed an increase in indirect methods. These methods are often sensor-based, capture data during the machining process, and employ various models, including emerging artificial intelligence techniques, for predicting tool wear. However, these methods have problems with environmental susceptibility, reduced reliability, limitations of application, and excessive costs. This paper suggests a tool wear integrated cutting load prediction model, tool wear detection, and fault diagnosis mechanism. The tool-wear-integrated cutting-load prediction model was constructed by combining the cutting-load prediction and tool-wear models. The coefficients of the model were derived from the actual cutting data extracted by the spindle load. Tool wear detection was implemented by dividing regions based on the tendency of the coefficient of the constructed tool wear integrated cutting load prediction model and the errors between the predicted and actual values. The proposed model demonstrated a performance comparable to that of the existing models in a single-cutting-condition path. However, it excelled in extracting the tool wear coefficients in paths with a mixture of various cutting conditions, which was not achievable with conventional models. Based on these coefficients, the cutting force was predicted with a maximum error of 3.3 %. Also, an accurate determination of the tool-wear regions was possible. Furthermore, the performance of the tool fault diagnosis method was validated using images of tools identified as being at risk of damage.
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