Intelligent Optimization of Laser Marking Parameters Using Neural Networks and Genetic Algorithms

Laser marking technology is essential in precision manufacturing, but optimizing its numerous parameters is com-plex and time-consuming with traditional methods. This study developed a fully automated laser marking system using the Draco series laser from Han's Laser, incorporating automatic loading and unloading mechanisms. By utilizing neural networks and genetic algorithms, we optimized laser marking on ABS materials. The research included COMSOL simulations of laser-material interactions, analysis of parameter variations on col-oration, temperature field distribution under laser irradiation, and prediction of marking effects for practical applications. The aim is to automate processes through algorithmic integration, providing an intelligent laser marking solution. The developed methodologies and algorithms are applicable to other manufac-turing optimizations, offering valuable insights for broader in-dustrial applications. Primary research focuses include COMSOL simulation and parameter analysis, developing an AI-integrated laser production line with color prediction, and exploring au-tomated laser marking on ABS material using neural networks and genetic algorithms.