Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

TL;DR

This paper introduces a novel approach for chatter detection in milling by leveraging topological features of vibration data combined with supervised machine learning, achieving high accuracy and robustness to noise.

Contribution

It presents the use of topological data analysis, specifically persistence diagrams transformed via Carlsson coordinates and template functions, for chatter diagnosis in milling.

Findings

Achieved up to 96% classification accuracy.

Demonstrated robustness of topological features to noise.

Validated approach on simulated milling data.

Abstract

Chatter detection has become a prominent subject of interest due to its effect on cutting tool life, surface finish and spindle of machine tool. Most of the existing methods in chatter detection literature are based on signal processing and signal decomposition. In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process. Persistence diagrams, a method of representing topological features, are not easily used in the context of machine learning, so they must be transformed into a form that is more amenable. Specifically, we will focus on two different methods for featurizing persistence diagrams, Carlsson coordinates and template functions. In this paper, we provide classification results for simulated data from various cutting configurations, including upmilling and downmilling, in addition to the same data with some added noise. Our results show that Carlsson Coordinates and Template Functions yield accuracies as high as 96% and 95%, respectively. We also provide evidence that these topological methods are noise robust descriptors for chatter detection.