Simulation of Predictive Control Model of Machining Error Based on Improved Particle Swarm Optimization Algorithm

The effect of machining is directly related to the growth of the whole industry, so it is of great significance to promote the growth of the industry through standardized technology. In machining, parts processing will be applied to multi-field production activities. If there are errors, it will directly affect the whole production process, so it is imperative to optimize and improve the technical errors of machining. It is an important method to predict and compensate the machining errors produced by the machining system to improve the machining accuracy. As far as the actual situation at this stage is concerned, good technological support is needed for the better growth of machining. In this article, the source and transmission mode of errors are analyzed. Aiming at the characteristics of high nonlinearity, multi-input and multi-output in machining process, a predictive control model of machining errors based on improved PSO-BPNN is proposed, and its superiority and universality are simulated and tested. The new algorithm can effectively reduce the overshoot of the system and shorten the adjustment time, so as to achieve better control effect, and has better tracking performance and adaptability.