Data-driven indirect punch wear monitoring in sheet-metal stamping processes
نویسندگان
چکیده
Abstract The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating use indirect methods to infer its condition. Past research approaches utilized a plethora machine learning models from suitable process signals, but have been limited by lack industrial-grade setups and sample sizes as well their insufficient interpretability. This work seeks address these limitations proposing sheared surface scrap web proxy for modeling quality acoustic emission signals. experimental was carried out an fine blanking setting. Evaluation model performances suggests that regression are capable relationship between signal features webs. Subsequent inference adhesive on root cause impairment represents most extensive effort monitoring both prediction perspective known authors.
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ژورنال
عنوان ژورنال: Journal of Intelligent Manufacturing
سال: 2023
ISSN: ['1572-8145', '0956-5515']
DOI: https://doi.org/10.1007/s10845-023-02129-w