Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

中文翻译：

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一种用于在线监测可变参数铣削表面粗糙度的深度迁移学习模型


表面粗糙度对于机械部件的功能和美学特性至关重要，在加工过程中必须仔细控制。然而，由于实验数据有限以及刀具磨损和振动等波动因素，在不同的加工参数下预测它具有挑战性。本研究开发了一个深度迁移学习模型，该模型结合了相关对齐方法和工具磨损，以增强模型泛化并降低数据采集成本。它利用多传感器数据和带有卷积块注意力模块 （CBAM-ResNet） 的 ResNet18 来提取特征，提高泛化和准确性，以监测不同条件下的铣削表面粗糙度。从不同的角度评估模型的性能。首先，通过使用 CBAM-ResNet 模型中的 CORAL 模块，所提出的模型在来自目标域的实验样本少于 500 个的情况下实现了高精度。这通过最大限度地减少不同数据集之间的二阶统计差异，证明了该模型强大的泛化能力。其次，消融实验表明，当结合 CORAL 和工具磨损时，测试误差显着减少，突出了它们对改进模型泛化的贡献。集成刀具磨损信息可显著减少各种传输条件下的测试误差，因为它反映了切削力、振动和积屑瘤形成的变化。第三，与现有深度迁移模型的比较进一步强调了所提出的方法在改进模型泛化方面的优势。 总之，所提出的表面粗糙度模型结合了刀具磨损和多传感器信号特征作为输入，并采用特征传输和 CBAM-ResNet，在各种加工参数中表现出卓越的泛化和准确性。