Workpiece Image-based Tool Wear Classification in Blanking Processes Using Deep Convolutional Neural Networks

TL;DR

This paper introduces a novel image-based method using deep convolutional neural networks to classify tool wear states in blanking processes, demonstrating high accuracy and potential for improved monitoring.

Contribution

It presents a new approach of using workpiece images and deep learning for tool wear classification, expanding beyond traditional force and acceleration signal methods.

Findings

High accuracy in wear state prediction

Potential for real-time tool wear monitoring

New research opportunities in manufacturing quality control

Abstract

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.