Advanced PID-controlled temperature prediction and management for optimized automated laser cutting processes

Abstract

In the context of increasing demands for precision and efficiency in industrial production and manufacturing, automated laser cutting technology plays a crucial role in addressing challenges related to processing parameter optimization, real-time monitoring, and intelligent control. This research introduces a novel approach to temperature prediction and control in automated laser cutting processes, leveraging advanced prediction techniques and a proportional-integral-derivative (PID) control algorithm. The study establishes a Gaussian distribution heat source model and applies the finite difference method to predict real-time temperature changes on the workpiece surface, ensuring high accuracy and reliability. The implementation of the PID algorithm allows for precise adjustments of laser parameters, such as power, pulse frequency, pulse width, and cutting speed, significantly enhancing temperature control. This approach not only improves cutting quality and efficiency but also reduces costs. The model's effectiveness is confirmed through validation with commercial finite element software and experimental outcomes. The results show that the accuracy of temperature prediction is kept at about 6%, and the accuracy of temperature control is kept within 5°C. This research contributes a new computational model that advances the optimization of automated laser cutting processes and supports the development of intelligent and adaptive control systems.