Taekwang Ha, Torgeir Welo, Geir Ringen, Jyhwen Wang
{"title":"基于人工神经网络的矩形管拉伸弯曲回弹智能控制","authors":"Taekwang Ha, Torgeir Welo, Geir Ringen, Jyhwen Wang","doi":"10.1115/1.4063737","DOIUrl":null,"url":null,"abstract":"Abstract Springback is one of the factors that causes decreased product quality in metal forming. Advanced 2D and 3D stretch bending process can be used to manufacture a complex geometry a profile with springback reduction. For a non-linear springback problem, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of the present work is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending, and its prediction and control performance is favorably compared to an ANN trained with only experimental data sets.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"56 1","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart Control of Springback in Stretch Bending of a Rectangular Tube by an Artificial Neural Network\",\"authors\":\"Taekwang Ha, Torgeir Welo, Geir Ringen, Jyhwen Wang\",\"doi\":\"10.1115/1.4063737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Springback is one of the factors that causes decreased product quality in metal forming. Advanced 2D and 3D stretch bending process can be used to manufacture a complex geometry a profile with springback reduction. For a non-linear springback problem, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of the present work is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending, and its prediction and control performance is favorably compared to an ANN trained with only experimental data sets.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4063737\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063737","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Smart Control of Springback in Stretch Bending of a Rectangular Tube by an Artificial Neural Network
Abstract Springback is one of the factors that causes decreased product quality in metal forming. Advanced 2D and 3D stretch bending process can be used to manufacture a complex geometry a profile with springback reduction. For a non-linear springback problem, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of the present work is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending, and its prediction and control performance is favorably compared to an ANN trained with only experimental data sets.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining