Intelligent tool wear recognition techniques have great significances to guide the cutting process in automated manufacturing systems. Traditional methods heavily rely on human experience and fail to consider the impact on processing. In this article, a novel deep kernel autoencoder (DKAE) feature learning method optimized by the gray wolf optimizer (GWO) is proposed for tool wear state intelligent recognition. The Gaussian kernel function is used to construct the loss function of the neural networks to enhance the feature learning ability. In addition, the current sensors are used to collect the multichannel spindle motor current signals in three directions of X-, Y-, and Z-axes. Compressed sensing technology is adopted to fuse and reduce the dimension of massive multichannel current signals into a single sample signal. A series of experiments with numerical control machines in the real manufacturing process of the impeller is run to test the superiority of tool wear recognition with this method. The results indicate that this work can be applied for real-time tool wear monitoring and greatly improved recognition accuracy.

中文翻译：

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基于多通道信号融合的深度核自编码器的刀具磨损识别


智能刀具磨损识别技术对于指导自动化制造系统中的切削过程具有重要意义。传统方法严重依赖人类经验，没有考虑对处理的影响。本文提出了一种由灰狼优化器（GWO）优化的新型深度核自动编码器（DKAE）特征学习方法，用于刀具磨损状态智能识别。采用高斯核函数构建神经网络的损失函数，增强特征学习能力。此外，电流传感器用于采集X、Y、Z轴三个方向的多路主轴电机电流信号。采用压缩传感技术，将海量多通道电流信号融合降维为单个采样信号。通过数控机床在叶轮实际制造过程中进行一系列实验，验证了该方法在刀具磨损识别方面的优越性。结果表明，该工作可应用于实时刀具磨损监测，并大大提高了识别精度。