Content area
Abstract
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.
Details
1 State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
2 Department of Electromechanical Science and Engineering, Zhengzhou University of Light Industry, Zhengzhou, China