  Blanking processes belong to the most widely used manufacturing techniques
due to their economic efficiency. Their economic viability depends to a large
extent on the resulting product quality and the associated customer
satisfaction as well as on possible downtimes. In particular, the occurrence of
increased tool wear reduces the product quality and leads to downtimes, which
is why considerable research has been carried out in recent years with regard
to wear detection. While processes have widely been monitored based on force
and acceleration signals, a new approach is pursued in this paper. Blanked
workpieces manufactured by punches with 16 different wear states are
photographed and then used as inputs for Deep Convolutional Neural Networks to
classify wear states. The results show that wear states can be predicted with
surprisingly high accuracy, opening up new possibilities and research
opportunities for tool wear monitoring of blanking processes.
