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

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