Adaptive cutting force simulation and intelligent optimization algorithm for efficient NC machining

Aiming at the problem of accurate prediction and optimization of cutting force in efficient NC machining, this paper proposes an adaptive cutting force simulation and intelligent optimization algorithm. The algorithm combines mechanism modeling and data-driven methods, and constructs a three-layer architecture simulation model based on improved Kienzle model, Transformer residual correction network and sliding window parameter calibration strategy, which can dynamically adapt to the changes of machining environment and realize high-precision cutting force prediction. At the same time, a hybrid optimization strategy combining genetic algorithm (GA), improved particle swarm optimization (PSO) and gradient descent method is designed. Through global and local collaborative optimization, the relationship between cutting force, machining cycle and tool wear is balanced, and the optimal combination of process parameters is quickly screened out. The experimental results show that the prediction error of cutting force of this method is as low as 3.7%∼7.9% under different working conditions, the optimization convergence time is significantly shortened, the material removal rate is increased to 1,532 mm3/min, the surface roughness Ra is improved to 0.78 μm, the tool life is extended to 11 pieces/blade, and the system has good real-time performance, which verifies its remarkable advantages in improving NC machining efficiency, quality and tool durability.