Suiyan Shang, G. Jiang, Zheng Sun, Wenwen Tian, Dawei Zhang, Jun Xu, C. Cheung
{"title":"用于数字孪晶机床行为映射的端面粗糙度预测","authors":"Suiyan Shang, G. Jiang, Zheng Sun, Wenwen Tian, Dawei Zhang, Jun Xu, C. Cheung","doi":"10.12688/digitaltwin.17819.1","DOIUrl":null,"url":null,"abstract":"Background: The quality of machined parts is considered as a relevant factor to evaluate the production performance of machine tools. For mapping the production performance into a digital twin machine tool, a virtual metrology model for surface roughness prediction, which affects products' mechanical capacity and aesthetic performance, is proposed in this paper. Methods: The proposed model applies a three-layer backpropagation neural network by using real-time vibration, force, and current sensor data collected during the end milling machining process. A grid search plan is used to settle down the number of neurons in the middle layer of the backpropagation neural network. Results: The experimental results indicate that the model with multiple signals as input performs better than it with a single signal. In detail, when the model input is the combination of force, vibration, and current sensor data, the prediction accuracy reaches the optimum with the mean absolute percentage error of 1.01%. Conclusions: Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Roughness prediction of end milling surface for behavior mapping of digital twined machine tools\",\"authors\":\"Suiyan Shang, G. Jiang, Zheng Sun, Wenwen Tian, Dawei Zhang, Jun Xu, C. Cheung\",\"doi\":\"10.12688/digitaltwin.17819.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: The quality of machined parts is considered as a relevant factor to evaluate the production performance of machine tools. For mapping the production performance into a digital twin machine tool, a virtual metrology model for surface roughness prediction, which affects products' mechanical capacity and aesthetic performance, is proposed in this paper. Methods: The proposed model applies a three-layer backpropagation neural network by using real-time vibration, force, and current sensor data collected during the end milling machining process. A grid search plan is used to settle down the number of neurons in the middle layer of the backpropagation neural network. Results: The experimental results indicate that the model with multiple signals as input performs better than it with a single signal. In detail, when the model input is the combination of force, vibration, and current sensor data, the prediction accuracy reaches the optimum with the mean absolute percentage error of 1.01%. Conclusions: Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.\",\"PeriodicalId\":29831,\"journal\":{\"name\":\"Digital Twin\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Twin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12688/digitaltwin.17819.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Twin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/digitaltwin.17819.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Roughness prediction of end milling surface for behavior mapping of digital twined machine tools
Background: The quality of machined parts is considered as a relevant factor to evaluate the production performance of machine tools. For mapping the production performance into a digital twin machine tool, a virtual metrology model for surface roughness prediction, which affects products' mechanical capacity and aesthetic performance, is proposed in this paper. Methods: The proposed model applies a three-layer backpropagation neural network by using real-time vibration, force, and current sensor data collected during the end milling machining process. A grid search plan is used to settle down the number of neurons in the middle layer of the backpropagation neural network. Results: The experimental results indicate that the model with multiple signals as input performs better than it with a single signal. In detail, when the model input is the combination of force, vibration, and current sensor data, the prediction accuracy reaches the optimum with the mean absolute percentage error of 1.01%. Conclusions: Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.
期刊介绍:
Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.
The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches.
The scope of Digital Twin includes, but is not limited to, the following areas:
● Digital twin concepts, architecture, and frameworks
● Digital twin theory and method
● Digital twin key technologies and tools
● Digital twin applications and case studies
● Digital twin implementation
● Digital twin services
● Digital twin security
● Digital twin standards
Digital twin also focuses on applications within and across broad sectors including:
● Smart manufacturing
● Aviation and aerospace
● Smart cities and construction
● Healthcare and medicine
● Robotics
● Shipping, vehicles and railways
● Industrial engineering and engineering management
● Agriculture
● Mining
● Power, energy and environment
Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.