Prediction of product roughness,profile,and roundness using machine learning techniques for a hard turning process  

作　　者：Chunling Du Choon Lim Ho Jacek Kaminski 

机构地区：[1]Advanced Remanufacturing and Technology Center(ARTC),Agency for Science,Technology and Research(A*STAR),Singapore

出　　处：《Advances in Manufacturing》2021年第2期206-215,共10页先进制造进展（英文版）

摘　　要：High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control(CNC)machining.Quality monitoring and prediction is of great importance to assure high-quality or zero defect production.In this work,we consider roughness parameter Ra,profile deviation Pt and roundness deviation RONt of the machined products by a lathe.Intrinsically,these three parameters are much related to the machine spindle parameters of preload,temperature,and rotations per minute(RPMs),while in this paper,spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters.Power spectral density(PSD)based feature extraction,the method to generate compact and well-correlated features,is proposed in details in this paper.Using the efficient features,neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness,0.86 for profile,and 0.95 for roundness.

关 键 词：Computer numerical control(CNC)machining Quality prediction Roughness parameter Profile deviation Roundness deviation Machine learning 

分 类 号：TG5[金属学及工艺—金属切削加工及机床] TP18[自动化与计算机技术—控制理论与控制工程]