Accurate tool wear prediction during machining is crucial to manufacturing since it will significantly influence tool life, machining efficiency, and workpiece quality. Although existing data-driven methods can achieve competitive performance in tool wear prediction, their main emphasis is on fixed operating conditions with sufficient training samples, which is impractical in engineering practice. This implies that predicting tool wear values under variable working conditions with insufficient data is still a challenge owing to the difference in data distributions in complex tool wear mechanisms. Besides, having no access to samples in new conditions is another challenge for tool wear prediction in engineering practice. To address these issues, we develop a hybrid information model-agnostic domain generalization (H-MADG) method to provide appropriate initial model parameters that can be fast adaptative to the new conditions after fine-tuning. Additionally, we construct hybrid information as model input by fusing process information with temporal properties derived by neural networks, and the hybrid information can offer more useful prior knowledge about the machining process. Experimental results on NASA milling data show that compared with contrastive techniques, the RMSE of the proposed H-MADG method is reduced by an average of 36.81 %, which can achieve a low average RMSE value of 0.0904 with 15 cases under five different network architectures. We also investigate several crucial impact factors of the H-MADG method and summarize corresponding analysis and suggestions.

中文翻译：

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智能制造的元学习方法：在各种操作条件下使用混合信息进行刀具磨损预测


加工过程中准确的刀具磨损预测对于制造至关重要，因为它将显着影响刀具寿命、加工效率和工件质量。尽管现有的数据驱动方法可以在刀具磨损预测方面取得有竞争力的性能，但它们主要强调具有足够训练样本的固定操作条件，这在工程实践中是不切实际的。这意味着，由于复杂刀具磨损机制中数据分布的差异，在数据不足的情况下预测可变工作条件下的刀具磨损值仍然是一个挑战。此外，无法在新条件下获取样本是工程实践中刀具磨损预测的另一个挑战。为了解决这些问题，我们开发了一种混合信息模型不可知域泛化（H-MADG）方法来提供适当的初始模型参数，这些参数可以在微调后快速适应新条件。此外，我们通过将过程信息与神经网络导出的时间属性融合来构建混合信息作为模型输入，并且混合信息可以提供有关加工过程的更有用的先验知识。 NASA铣削数据的实验结果表明，与对比技术相比，所提出的H-MADG方法的RMSE平均降低了36.81%，在5种不同网络架构下的15个案例中可以实现0.0904的低平均RMSE值。我们还调查了H-MADG方法的几个关键影响因素，并总结了相应的分析和建议。