In the process of metal cutting, realizing effective monitoring of tool wear is of great significance to ensure the quality of parts machining. To address the tool wear monitoring (TWM) problem, a tool wear monitoring method based on data-driven and physical output is proposed. The method divides two Physical models (PM) into multiple stages according to the tool wear in real machining scenarios, making the coefficients of PM variable. Meanwhile, by analyzing the monitoring capabilities of different PMs at each stage and fusing them, the PM's ability to deal with complex nonlinear relationships, which is difficult to handle, is improved, and the flexibility of the model is improved; The pre-processed signal data features were extracted, and the original features were fused and downscaled using Stacked Sparse Auto-Encoder (SSAE) networker to build a data-driven model (DDM). At the same time, the DDM is used as a guidance layer to guide the fused PM for the prediction of wear amount at each stage of the tool, which enhances the interpretability of the monitoring model. The experimental results show that the proposed method can realize the accurate monitoring of tool wear, which has a certain reference value for the flexible tool change in the actual metal-cutting process.

中文翻译：

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一种基于数据驱动和物理输出的刀具磨损监测方法


在金属切削过程中，实现刀具磨损的有效监测对于保证零件加工质量具有重要意义。为了解决刀具磨损监测（TWM）问题，提出了一种基于数据驱动和物理输出的刀具磨损监测方法。该方法根据实际加工场景中刀具的磨损情况，将两个物理模型（PM）分为多个阶段，使PM的系数可变。同时，通过分析各个阶段不同PM的监测能力并进行融合，提高了PM处理难以处理的复杂非线性关系的能力，提高了模型的灵活性；提取预处理的信号数据特征，并使用堆叠稀疏自动编码器（SSAE）网络器对原始特征进行融合和缩小，以构建数据驱动模型（DDM）。同时，利用DDM作为引导层，引导融合PM进行刀具各阶段磨损量的预测，增强了监测模型的可解释性。实验结果表明，该方法能够实现刀具磨损的准确监测，对于实际金属切削过程中的柔性换刀具有一定的参考价值。