Optimal selection of control parameters for automatic machining based on BP neural network

• Published in: Energy reports 2022-11, Vol.8, p.7016-7024
• Main Author: Liu, Hao
• Format: Article
• Language: English
• Subjects: Automatic mechanical processing; BP neural network; Elliptical pit; Parameter optimization
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.05.038

Automatic selection of parameters in machining has always been the bottleneck of computer aided process planning (CAPP). The duplication phenomenon in the processing of axle parts affects the processing accuracy of the parts, but there are many factors affecting the processing accuracy, and it is not easy to determine the specific factors. Therefore, there is no absolutely accurate formula to calculate how to process to achieve the required accuracy. Based on the above background, the aim of this paper is to optimize the parameters of automatic machining control through BP neural network. In view of the practical problems of this research, this work uses the BP network as a search tool. The BP network has been trained using large amounts of test data. Several factors that affect the occurrence of malignancy are studied and analyzed. The basic method of solving the problem of reproduction by using BP network is also discussed, and the feasibility of this method is preliminarily established. The automatic processing of elliptical socket material based on BP neural network is introduced, cutting with different pre-tangential radial errors. The optimization results of parameters are as follows: spindle speed n=5000 r/min, feed speed f=0.2mm/s, swing angular velocity is ω=0.53°/s. The surface roughness of elliptical socket processed by BP neural network is up to Ral.6 combined with the adjusted parameters of socket shape.