{"title":"基于位姿的铣削机器人切削力辨识","authors":"Maxiao Hou, Hongru Cao, Yang Luo, Yanjie Guo","doi":"10.1115/1.4062145","DOIUrl":null,"url":null,"abstract":"\n Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pose-dependent Cutting Force Identification for Robotic Milling\",\"authors\":\"Maxiao Hou, Hongru Cao, Yang Luo, Yanjie Guo\",\"doi\":\"10.1115/1.4062145\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062145\",\"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":"5","ListUrlMain":"https://doi.org/10.1115/1.4062145","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}
Pose-dependent Cutting Force Identification for Robotic Milling
Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.
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