Si Sumei, Mu Deqiang, Tang Hailiang
School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel). 2025 May 6;25(9):2935. doi: 10.3390/s25092935.
In grinding machining, monitoring grinding wheel wear is essential for ensuring process quality wear and reducing production costs. This paper presents a hybrid CBiGRUPE model to predict grinding wheel wear, which integrates the advantages of convolutional neural networks (CNNs), bidirectional gated recurrent unit (BiGRU), and the Performer encoder. Time-domain features are extracted from the spindle motor current signals of a surface grinding machine. The structure and hyperparameters of CBiGRUPE are optimized using Bayesian optimization. Experimental validation of the model demonstrates superior performance, with mean absolute error (), root mean square error (), and coefficient of determination () values of 3.041, 3.927, and 0.920, respectively. Compared to models like CNN, BiGRU, and Transformer, the CBiGRUPE model offers more accurate and stable wear predictions. This paper also discusses the advantages and limitations of various models for estimating grinding wheel wear, emphasizing the effectiveness of the proposed approach. This study establishes a foundation for compensating wheel wear and accurately determining the optimal dressing time.
在磨削加工中，监测砂轮磨损对于确保加工质量和降低生产成本至关重要。本文提出了一种混合CBiGRUPE模型来预测砂轮磨损，该模型整合了卷积神经网络（CNN）、双向门控循环单元（BiGRU）和Performer编码器的优点。从平面磨床的主轴电机电流信号中提取时域特征。使用贝叶斯优化对CBiGRUPE的结构和超参数进行优化。该模型的实验验证表明其具有卓越的性能，平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R²）值分别为3.041、3.927和0.920。与CNN、BiGRU和Transformer等模型相比，CBiGRUPE模型提供了更准确、稳定的磨损预测。本文还讨论了各种用于估计砂轮磨损的模型的优缺点，强调了所提方法的有效性。本研究为补偿砂轮磨损和准确确定最佳修整时间奠定了基础。
