Something like normal functionality of tools in a manufacturing process is typically designed to ensure reliability, where fast and accurate identification of tool abnormal operation plays a vital role in intelligent manufacturing. In this study, a novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM). The analysis benefits from the use of output power signals from the cutting tool, since they can be obtained easily and efficiently, enabling the proposed method to be applicable in practical operation for online condition monitoring. Moreover, effectiveness of the proposed method is investigated, using test data from cutting tools at various tool wear conditions. Results demonstrate that with the proposed method, tool wear condition can be identified accurately and efficiently. Furthermore, with test data collected at cutting tools with different sizes, the robustness of the proposed method can be further clarified.

中文翻译：

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使用输出功率信号的在线工具状态监控的改进的深度学习模型

在制造过程中，通常会设计出类似于工具的正常功能之类的功能来确保可靠性，其中快速，准确地识别工具异常操作在智能制造中起着至关重要的作用。在这项研究中，提出了一种评估刀具状态的新方法，该方法包括一个具有较宽的第一层内核（W-CONV）和长短期记忆（LSTM）的卷积神经网络。该分析得益于使用来自切削工具的输出功率信号，因为可以轻松，高效地获得它们，从而使该方法可应用于在线状态监测的实际操作中。此外，使用来自各种刀具磨损条件下切削刀具的测试数据，研究了该方法的有效性。结果表明，利用所提出的方法，可以准确有效地识别刀具磨损状况。此外，利用在不同尺寸的切削工具上收集的测试数据，可以进一步阐明所提出方法的鲁棒性。