Theoretical and empirical coupled modeling on the surface roughness in diamond turning

作     者：He, C. L. Zong, W. J. Cao, Z. M. Sun, T. 

作者机构：Harbin Inst Technol Ctr Precis Engn Harbin 150001 Peoples R China 

出 版 物：《MATERIALS & DESIGN》 (Mater. Des.)

年 卷 期：2015年第82卷第Oct.5期

页      面：216-222页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [HIT.BRETIII.201412] Major Special Subject of High-end CNC Machine Tools and Basic Manufacturing Equipment Science and Technology of China [2011ZX04004-031] 

主　　题：Diamond turning Surface roughness Minimum undeformed chip thickness RBF neural network PSO algorithm 

摘      要：In this work, a theoretical and empirical coupled method is proposed to predict the surface roughness achieved by single point diamond turning, in which the surface roughness is considered to be composed of the certain and uncertain parts. The certain components are directly formulated in theory, such as the effects of the kinematics and minimum undeformed chip thickness. The uncertain components in relation to the material spring back, plastic side flow, micro defects on the cutting edge of diamond tool, and others, are empirically predicted by a RBF (radial basis function) neural network, which is established by referring to the experimental data. Finally, the particle swarm optimization algorithm is employed to find the optimal cutting parameters for the best surface roughness. The validation experiments show that the optimization is satisfied, and the prediction accuracy is high enough, i.e. that the prediction error is only 0.59-10.11%, which indicates that the novel surface roughness prediction method proposed in this work is effective. (C) 2015 Elsevier Ltd. All rights reserved.