Vigilancia tecnológica

The accuracy of the acoustic signal prediction model for wood–plastic composites milling has an important influence on the condition monitoring of the cutting process and the improvement of the machining environment. To establish a high-precision prediction model of sound signal in the high-speed milling of wood–plastic composites, high-speed milling experiments on self-developed wood–plastic composites were carried out with cemented carbide tools. A mathematical model of the relationship of the four milling parameters, including axial cutting depth, radial cutting depth, feed rate and cutting speed, and the sound signal of wood–plastic composites milling, was established by using the full-factor test method. The experimental data obtained by the orthogonal test method were used as the test samples in the mathematical model. Test results show that the prediction accuracy of the mathematical model of the sound signal in the milling of wood–plastic composites exceeds 95.4%. To further improve the prediction accuracy of the sound signal in the milling of wood–plastic composites, a prediction model was established using back propagation (BP) neural network. Then, the particle swarm optimization (PSO) algorithm was used to optimize the BP neural network, obtaining the PSO–BP neural network prediction model. The test results show that the prediction accuracy of the PSO–BP prediction model for the sound signal in the high-speed milling of wood–plastic composites exceeds 97.5%. The PSO–BP model has a better global approximation ability and higher prediction accuracy than the BP model. The research results can provide a reference basis for sound signal prediction in the high-speed milling of wood–plastic composites.