首页 | 本学科首页   官方微博 | 高级检索  
     


On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression
Affiliation:1. CSIR—Central Mechanical Engineering Research Institute, Durgapur 713209, WB, India;2. Mechanical Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721302, WB, India;1. Department of Computer Science, and Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States;2. Department of Computer Science, Virginia Tech, Blacksburg, VA, United States;1. School of Mechanical Engineering, Vignan University, AP, India;2. Department of Mechanical Engineering, GIT, GITAM University, AP, India;3. Department of Mechanical Engineering, JNTUK, AP, India;1. Department of Production Engineering, UTP University of Science and Technology, Al. prof. S. Kaliskiego 7, Bydgoszcz 85-796, Poland;2. Department of Computer Methods, UTP University of Science and Technology, Al. prof. S. Kaliskiego 7, Bydgoszcz 85-796, Poland;3. Laboratory of Machine Design, Lappeenranta University of Technology, Skinnarilankatu 34, Lappeenranta 53850, Finland;4. Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia;1. West Pomeranian University of Technology, Szczecin, Poland;2. Maritime University of Szczecin, Poland;1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA;2. Arts et Métiers ParisTech, Aix-en-Provence, 13617, France
Abstract:In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.
Keywords:Tool flank wear prediction  Machine vision  GLCM  Voronoi tessellation  Wavelet transform  Support vector regression
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号