基于动态惯性权重粒子群算法的磨削低能耗加工方法

利用三层误差反向传播(back propagation, BP)神经网络建立磨削能耗预测模型，以砂轮线速度、进给速度和磨削深度为影响因素设计125组全因子试验，并取其中的75组试验数据作为该预测模型的训练样本与测试样本。采用动态惯性权重改进粒子群算法(adaption particle swarm optimization, APSO)，以BP神经网络的预测作为适应度函数，以最小能耗为目标进行迭代寻优获取最优工艺参数。结果表明:模型预测结果较为准确，采用优化后的工艺参数能够有效降低磨削能耗。 

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.