Research on MCVE piston machining and process parameter optimization

• Published in: International journal of advanced manufacturing technology Vol. 93; no. 9-12; pp. 3955 - 3966
• Main Authors: Zhang, Yong, Huang, Yu, Shao, WenJun, Ming, Wuyi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2017; Springer Nature B.V
• Subjects: Artificial neural networks; Back propagation; Back propagation algorithms; Back propagation networks; CAE) and Design; Computer-Aided Engineering (CAD; Data models; Ellipticity; Engineering; Frequency response; Genetic algorithms; Industrial and Production Engineering; Machining; Mathematical models; Mechanical Engineering; Media Management; Neural networks; Optimization; Original Article; Pistons; Process parameters; Surface roughness; Turning (machining); Working conditions; MCVE; BPNN; Piston machining; GA; FTS
• DOI: 10.1007/s00170-017-0838-4
• ISSN: 0268-3768

A piston is an important part of an engine. Its shape is designed into middle-convex and varying ellipse (MCVE) to adapt to the complex working environment. The main requirements of MCVE piston machining are high frequency response, small range tool motion, and high precision. In this article, an MCVE data model is established for the piston profile design, and the turning principle and control procedure are discussed to develop a fast tool servo (FTS) system for piston turning. In the end, back propagation neural network (BPNN) and genetic algorithm (GA) are combined to optimize the process parameters in the MCVE piston machining, which includes general turning parameters and special MCVE turning parameters. Through the experiments and BPNN-GA optimization, the ellipticity error ( E ) and surface roughness ( Ra ) of all pistons met the design requirements. According to verification experiments, the optimization results of E and Ra are 3.04 and 1.204 μm, respectively, and their relative errors are 10.13 and 4.27%, respectively. It has been proved that the MCVE data model and the control design of FTS are feasible and can effectively produce MCVE piston; the BPNN-GA optimization method is obviously effective and can improve processing effect and machining efficiency.