Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

刀具磨损监控在智能制造中日益重要，以提高加工效率。多域特征可以有效地表征工具的磨损状况，但是手动特征融合会降低监视效率，并阻碍进一步提高预测精度。为了克服这些不足，提出了一种新的基于深度卷积神经网络（DCNN）的基于多域特征融合的刀具磨损预测方法。在这种方法中，分别从多感官信号（例如三维切削力和振动）中提取多域（包括时域，频域和时频域）特征作为工具磨损状况的健康指标，然后基于设计的DCNN模型直接建立这些特征与实时刀具磨损之间的关系，以将自适应特征融合与自动连续预测相结合。该工具磨损预测方法的性能通过使用三个刀具运行失败数据集进行了实验验证，该数据是从干铣削操作下高速CNC机床的三刃球头硬质合金刀具测量的。实验结果表明，该方法的预测精度明显高于其他先进方法。该工具磨损预测方法的性能通过使用三个刀具运行失败数据集进行了实验验证，该数据是从干铣削操作下高速CNC机床的三刃球头硬质合金刀具测量的。实验结果表明，该方法的预测精度明显高于其他先进方法。该工具磨损预测方法的性能通过使用三个刀具运行失败数据集进行了实验验证，该数据是从干铣削操作下高速CNC机床的三刃球头硬质合金刀具测量的。实验结果表明，该方法的预测精度明显高于其他先进方法。