Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits

TL;DR

This paper presents a low-cost acceleration sensor-based method for condition monitoring and failure prediction in specialized milling machines, demonstrating practical applicability with limited training data.

Contribution

It introduces a sensor retrofit approach for special purpose milling machines and explores supervised failure detection methods suitable for limited data scenarios.

Findings

Effective detection of blade wear and breakage

Successful identification of improper mounting and belt tension issues

Low-cost retrofitting enables practical condition monitoring

Abstract

Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard milling machines, while machinery designed for more specialized tasks has gained only limited attention so far. This paper demonstrates the application of an acceleration sensor to allow for convenient condition monitoring of such a special purpose machine, i.e. round seam milling machine. We examine a variety of conditions including blade wear and blade breakage as well as improper machine mounting or insufficient transmission belt tension. In addition, we presents different approaches to supervised failure recognition with limited amounts of training data. Hence, aside theoretical insights, our analysis is of high, practical importance, since retrofitting older machines with acceleration sensors and an on-edge classification setup comes at low cost and effort, yet provides valuable insights into the state of the machine and tools in particular and the production process in general.