Research on Laser Cutting Quality Optimization Strategy Based on Intelligent Control Technology

Abstract

In order to improve the processing quality and control accuracy of laser cutting process under complex working conditions, this paper proposes a laser cutting quality optimization strategy based on intelligent control technology. By constructing a multi-module collaborative system including optical regulation, parameter prediction, visual monitoring and adaptive feedback control, dynamic perception and real time adjustment of laser beam energy distribution and cutting process state are realized. The system uses BP neural network to model and predict process parameters and quality indicators, and combines fuzzy PID algorithm to construct a closed-loop feedback mechanism. Comparative experiments were carried out on 2mm thick 304 stainless steel plates. The results show that the intelligent control system is superior to the traditional system in terms of surface roughness, kerf width, slag rate and cutting efficiency. The maximum efficiency is increased by 27% and the roughness is reduced by more than 40%. Studies have shown that the system has good stability and adaptability and has significant prospects for engineering application.