针对不同砂轮磨削性能对内圆切入磨削加工质量具有重要影响，为了实现在线监测内圆磨削加工过程中不同砂轮在相同实验参数条件下进行磨削时的磨削性能，提出了一种基于粒子群优化-反向传播（particle swarm optimization-back propagation, PSO-BP）神经网络的不同砂轮磨削性能监测方法。首先，对采集的声发射信号、功率信号、振动信号、位移信号以及电流信号的特征参数进行特征提取；然后，根据各传感器的特征值数据样本及PSO-BP神经网络的全局寻优功能，采用初始权值和阈值，建立了PSO-BP在线监测模型对不同砂轮磨削性能进行精准监测；最后，结合实验数据将BP神经网络模型与PSO-BP模型进行了对比分析。结果表明PSO-BP监测模型比BP神经网络模型监测精度更高，平均正确率高达97.6%，并通过大量试验验证了PSO-BP神经网络模型的有效性，能够有效监测不同砂轮的磨削性能状态。

The quality of internal plunge grinding process is affected by the grinding performance of different grinding wheels. In order to online monitor the grinding performance of different grinding wheels under the same experimental parameters during the internal grinding process. A particle swarm optimization-back propagation (PSO-BP) neural network-based grinding performance monitoring method for different grinding wheels was proposed. Firstly, the feature parameters of acoustic emission signal, power signal, vibration signal, displacement signal and current signal were extracted. Then, according to the eigenvalue data samples of each sensor and the global optimization function of BP neural network by particle swarm optimization algorithm, the PSO-BP online monitoring model was established by using PSO algorithm to optimize the initial weights and thresholds of BP neural network to accurately monitor the grinding performance of different grinding wheels. Finally, the BP neural network model and the PSO-BP model were analyzed and compared with the experimental data. The results show that the PSO-BP monitoring model has higher monitoring accuracy than the BP neural network model, with an average correct rate as high as 97.6%, and the validity of PSO-BP is verified through a large number of experiments, which is able to effectively monitor the grinding performance status of different grinding wheels.