A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials  

作　　者：Chunlei He Jiwang Yan Shuqi Wang Shuo Zhang Guang Chen Chengzu Ren 

机构地区：[1]Tianjin Key Laboratory of Equipment Design and Manufacturing Technology,Department of Mechanical Engineering,Tianjin University,Tianjin 30054,People’s Republic of China [2]Department of Mechanical Engineering,Keio University,Yokohama 223-8522,Japan [3]Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System,School of Mechanical Engineering,Tianjin University of Technology,Tianjin 300384,People’s Republic of China [4]School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,People’s Republic of China

出　　处：《International Journal of Extreme Manufacturing》2023年第3期620-644,共25页极端制造（英文）

基　　金：National Natural Science Foundation of China(Nos.52175430,51935008 and 52105478);China National Postdoctoral Program for Innovative Talents(BX20200234);Open Fund of Tianjin Key Laboratory of Equipment Design and Manufacturing Technology(EDMT)for the support of this work。

摘　　要：Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface fi

关 键 词：diamond turning material-defect roughness component polycrystalline copper neural network simulated annealing algorithm 

分 类 号：TQ163[化学工程—高温制品工业] TP181[自动化与计算机技术—控制理论与控制工程]