Enhancing mechanical design, manufacturing, and automation through ai-based computer numerical control (CNC) optimization

Resumo

The rapid advancements in Artificial Intelligence (AI) have provided new opportunities for optimizing Computer Numerical Control (CNC) technology, crucial for mechanical design, manufacturing, and automation. This study explores AI-driven optimization strategies in CNC systems to enhance machining efficiency, surface quality, tool life, and overall operational stability. The methodology integrates three AI technologies: reinforcement learning (RL) for tool path planning optimization, particle swarm optimization (PSO) for cutting parameter adjustment, and fuzzy logic for controlling abnormal situations. By using these techniques, the study aims to address inefficiencies in traditional methods and improve the adaptability and automation of CNC systems. A series of comparative experiments conducted on a DMG Mori DMU 50 five-axis CNC machining center demonstrate significant improvements in surface roughness, machining speed, tool wear rate, and energy consumption. The results highlight the potential of AI-based optimization in improving CNC machining performance, paving the way for more efficient, sustainable, and intelligent manufacturing processes. Future research should focus on further refining optimization algorithms for various materials and explore the integration of CNC technology with emerging technologies such as industrial internet and big data.