Prediction and Simulation Design of Cutting Force of Machine Tool Based on Fuzzy Clustering Algorithm

The cutting process is a dynamic stochastic process, which is jointly affected by variable and time-varying cutting conditions and quantifiable and difficult to quantify and predict influencing factors. This article uses fuzzy clustering algorithm to predict and model cutting force. To overcome the shortcomings of FCM (fuzzy c-means) algorithm and improve clustering quality, a new fuzzy clustering algorithm is proposed using PSO (Particle swarm optimization) and FCM algorithm to achieve better prediction of cutting force Firstly, by choosing a reasonable particle initialization space, the sensitivity to the initial clustering center is reduced and the convergence speed is improved. Secondly, by optimizing the maximum velocity of parameter particle motion and introducing the neighborhood of ring topology structure, the defect of premature convergence of PSO algorithm is solved. The simulation results show that the error between experimental data and predicted data is basically in an acceptable range, and the prediction trend is getting better and better, and the predicted results can also meet the measured values in actual cutting. The experimental results prove the effectiveness of FCM in cutting force prediction of CNC machine tools. It is also of great significance for energy-saving machining and process optimization of CNC machine tools.