Reduce grinding energy consumption by modified particle swarm optimization based on dynamic inertia weigh

A three-layer back propagation (BP) neural network was used to establish a grinding energy consumption prediction model. 125 single-factor experiments were designed with the grinding wheel linear velocity, feed rate and grinding depth of cut as the influencing factors. 75 sets of experimental data were obtained as the training samples and test samples of the prediction model. Particle swarm optimization algorithm was improved by using adaptive dynamic inertia weight (adaption particle swarm optimization, APSO), and the prediction of BP neural network was used as fitness function. The optimal process parameters were obtained by iterative optimization aiming at minimum energy consumption. The results show that the prediction model is accurate and the optimized process parameters can effectively reduce the grinding energy consumption.