Description
This dissertation introduces a framework for indirect tool condition monitoring in sheet-metal stamping using acoustic emission signals. Analysis of 398, 047 fineblanking strokes from industrial and laboratory settings revealed characteristic signal patterns during wear progression. Using scrap web surface quality as a punch wear proxy, machine learning models were trained and analyzed via explainable AI techniques, leading to a scalar monitoring indicator validated across industrial datasets.
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