A grinding force prediction model for SiCp/Al composite based on single-abrasive-grain grinding

Grinding is the main processing method for particle-reinforced composites, and grinding force prediction models are very important for research on removal mechanisms. In this study, single-abrasive-grain grinding experiments on SiCp/Al composites were conducted to determine the grinding forces at different grinding process parameters. In addition, a prediction model for the single-abrasive-grain grinding force was established to study the influence of the grinding process parameters and grinding grain angle on the grinding force of SiCp/Al composite. Moreover, multi-abrasive-grain grinding experiments were conducted at different grinding process parameters, which resulted in different grinding forces. The support vector machine (SVM) prediction method based on particle swarm optimisation (PSO) was used to establish a prediction model for the multi-abrasive-grain grinding force; the single-abrasive-grain grinding forces at different grinding grain angles were the input, and the average experimental grain grinding force was the output. The results show that the error between the predicted and experimental grinding forces is below 12%. Furthermore, the grinding force decreases with increasing wheel speed and increases with increasing feed velocity and grinding depth. The PSO-SVM algorithm-based grinding force prediction model can accurately predict the grinding force of SiCp/Al composite and provides theoretical support for improved surface quality.