Strategy of CNC Machine Tool Optimized with Integrated Genetic Algorithm

Computer numerical control (CNC) machining plays a crucial role in contemporary manufacturing with high precision and automation. The conventional CNC programming techniques, though, do not optimize tool path planning, and hence machining time, energy utilization, and tool wear are increased. This paper introduces a genetic algorithm (GA)-based optimization approach to minimize the cutting tool travel path (CTTP) in CNC machining. The CTTP problem is represented as a type of traveling salesman problem (TSP), with the machining operations represented as nodes and the goal being to determine the minimum tool path length while respecting operational constraints. The GA-based optimization framework employs effective chromosome representation for operation order, a multi-objective fitness function, and adaptive crossover and mutation operators. Experimental confirmation was performed on two case studies: a low-scale CNC machining facility with restricted operations and a high-scale facility with numerous planes of cutting. The outcome illustrated notable enhancements in machining effectiveness. Particularly, CTTP decreased by 12% in Case Study 1 and by 13% in Case Study 2. Machining time decreased by 36% in Case Study 1 and by 33% in Case Study 2. The energy efficiency was also enhanced with a 12% reduction in energy consumption in Case Study 1 and 13% in Case Study 2. The GA-based optimization performed better compared with conventional techniques with a 30%–40% decrease in machining time, improving tool life, and ensuring accuracy. Future research will investigate hybrid optimization methods, such as deep learning and reinforcement learning, for real-time process adjustment.