Development of Robust Machine Learning Models for Tool-Wear Monitoring in Blanking Processes Under Data Scarcity

Tool wear is a major challenge in sheet-metal forming, as it directly affects product quality and process stability. Reliable monitoring of tool-wear conditions is therefore essential, yet it remains challenging due to limited data availability and uncertainties in manufacturing conditions. To this...

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Egile Nagusiak:	Johannes Hofmann, Ciarán-Victor Veitenheimer, Chenkai Fei, Chengting Chen, Haoyu Wang, Lianhao Zhao, Peter Groche
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-09-01
Saila:	Applied Sciences
Gaiak:	blanking condition monitoring uncertainty machine learning
Sarrera elektronikoa:	https://www.mdpi.com/2076-3417/15/19/10323
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