A Study on Industrial Tool Wear: Assessing the Potential for Optimised Lifespan Utilisation of Indexable Inserts Through Automated Wear Detection

Abstract

This research focuses on the development of an autonomous wear detection system for indexable inserts used in manufacturing processes, with the goal of optimising tool life. The study addresses the challenge of premature tool replacement in an industrial setting, where tool lifespan is often underutilised. A prototype machine vision system was designed and tested, incorporating an 8.9 MP monochrome camera, macro lens, and deep learning software to measure flank wear. The system was validated against high-precision microscopy, revealing an average deviation of 27.4 µm. Analysis of 676 cutting edges of indexable inserts from an Austrian machine manufacturer confirmed that many inserts were retired with significant remaining lifespan, suggesting that tools are frequently replaced after only 50-60% of their potential use. The study highlights the viability of integrating such systems into industrial environments, with potential economic and environmental benefits. Future work will focus on improving measurement accuracy and expanding the dataset to further validate the hypothesis across different manufacturers and industries.