Reliable tool wear prediction is of great importance for the improvement of machining quality and efficiency. With the advent of the big data era, data-driven tool wear prediction methods have proven to be highly effective. However, these methods have also revealed issues such as shallow feature extraction and limited generalization of models across different machining processes. The objective of this research is to propose a tool wear prediction method based on multimodal information fusion and deep subdomain adaptation to solve the existing problems. First, the original one-dimensional time-series tool monitoring signals are encoded into images to generate a two-dimensional image dataset. Secondly, a two-channel prediction model combining Residual Network and Gated Recurrent Unit is constructed to extract features from the two-dimensional image signals and the one-dimensional time-series signals respectively, and the extracted spatial and temporal features are fused. Thirdly, the dataset is divided into subdomains based on wear values, and the generalization ability of the model is improved by reducing the feature differences between source and target domains through the subdomain adaptive method, thus achieving the prediction of the tool wear values under different situations. Finally, through the validation on two milling wear datasets and comparison with the prediction results of other models, the experimental results prove the accuracy and good generalization of the method, which can provide a reference to improve the machining quality and efficiency, and is suitable for practical industrial application scenarios.

中文翻译：

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基于多模态信息融合和深度子域自适应的新型刀具磨损预测方法


可靠的刀具磨损预测对于提高加工质量和效率非常重要。随着大数据时代的到来，数据驱动的刀具磨损预测方法已被证明非常有效。然而，这些方法也揭示了诸如浅特征提取和模型在不同加工过程中的泛化有限等问题。本研究的目的是提出一种基于多模态信息融合和深度子域适应的刀具磨损预测方法来解决现有问题。首先，将原始的一维时间序列工具监控信号编码成图像，生成二维图像数据集;其次，构建残差网络和门控循环单元相结合的双通道预测模型，分别从二维图像信号和一维时间序列信号中提取特征，并将提取的空间和时间特征进行融合。再次，根据磨损值将数据集划分为子域，通过子域自适应方法减少源域和目标域之间的特征差异，从而提高模型的泛化能力，从而实现不同情况下刀具磨损值的预测。最后，通过在两个铣削磨损数据集上的验证并与其他模型的预测结果进行比较，实验结果证明了该方法的准确性和良好的泛化性，可为提高加工质量和效率提供参考，适用于实际工业应用场景。