An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process

TL;DR

This paper evaluates three conventional anomaly detection methods for real-time tool wear monitoring in CNC turning processes, demonstrating their effectiveness and robustness through experimental data and proposing an optimized algorithm for accurate wear prediction.

Contribution

It introduces a real-time anomaly detection algorithm optimized for tool wear prediction, demonstrating robustness and ease of application in CNC machining environments.

Findings

High accuracy in tool wear prediction

Multivariate analysis enhances robustness

Algorithm performs well across different scenarios

Abstract

We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines. The three-dimensional force data streams we used were extracted from a turning experiment of 21 runs for which a tool was run until it generally satisfied an end-of-life criterion. Our real-time anomaly detection algorithm was scored and optimised according to how precisely it can predict the progressive wear of the tool flank. Most of our tool wear predictions were accurate and reliable as illustrated in our off-line simulation results. Particularly when the multivariate analysis was applied, the algorithm we develop was found to be very robust across different scenarios and against parameter changes. It shall be reasonably easy to apply our approach elsewhere for real-time tool wear analytics.