Abstract

The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating the use of indirect methods to infer its condition. Past research approaches utilized a plethora of machine learning models to infer the punch wear state from suitable process signals, but have been limited by the lack of industrial-grade process setups and sample sizes as well as their insufficient interpretability. This work seeks to address these limitations by proposing the sheared surface of the scrap web as a proxy for the punch wear and modeling its quality from acoustic emission signals. The experimental work was carried out in an industrial-grade fine blanking process setting. Evaluation of the model performances suggests that the utilized regression models are capable of modeling the relationship between acoustic emission signal features and sheared surface quality of the scrap webs. Subsequent model inference suggests adhesive wear on the punch as a root cause for the sheared surface impairment of the scrap webs. This work represents the most extensive modeling effort on indirect punch wear monitoring in sheet-metal stamping both from a model prediction and model inference perspective known to the authors.

中文翻译：

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金属板材冲压过程中数据驱动的间接冲头磨损监测

摘要

钣金冲压过程中冲头的磨损状态无法直接观察，需要采用间接方法来推断其磨损状况。过去的研究方法利用大量机器学习模型从合适的工艺信号推断冲头磨损状态，但由于缺乏工业级工艺设置和样本大小以及可解释性不足而受到限制。这项工作旨在通过提出废料腹板的剪切表面作为冲头磨损的代理并根据声发射信号对其质量进行建模来解决这些限制。实验工作是在工业级精冲工艺设置中进行的。模型性能的评估表明，所使用的回归模型能够对声发射信号特征与废幅材的剪切表面质量之间的关系进行建模。随后的模型推断表明，冲头上的粘着磨损是废料腹板剪切表面损伤的根本原因。这项工作代表了从作者已知的模型预测和模型推理角度来看，在金属板材冲压中间接冲头磨损监测方面最广泛的建模工作。