Topological Feature Vectors for Chatter Detection in Turning Processes

Abstract

Machining processes are most accurately described using complex dynamical systems that include nonlinearities, time delays, and stochastic effects. Due to the nature of these models as well as the practical challenges which include time-varying parameters, the transition from numerical/analytical modeling of machining to the analysis of real cutting signals remains challenging. Some studies have focused on studying the time series of cutting processes using machine learning algorithms with the goal of identifying and predicting undesirable vibrations during machining referred to as chatter. These tools typically decompose the signal using Wavelet Packet Transforms (WPT) or Ensemble Empirical Mode Decomposition (EEMD). However, these methods require a significant overhead in identifying the feature vectors before a classifier can be trained. In this study, we present an alternative approach based on featurizing the time series of the cutting process using its topological features. We first embed the time series as a point cloud using Takens embedding. We then utilize Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting classifier combined with feature vectors derived from persistence diagrams, a tool from persistent homology, to encode chatter's distinguishing characteristics. We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental turning data. Our results show that in two out of four cutting configurations the TDA-based features yield accuracies as high as 97%. We also show that combining Bezier curve approximation method and parallel computing can reduce runtime for persistence diagram computation of a single time series to less than a second thus making our approach suitable for online chatter detection.

Implementations of parallel computing and Bezier curve approximation for persistence diagram computation are added into the manuscript. Abstract and results section are updated with respect to the results obtained from persistence diagrams computed with Bezier approximation

References in corpus (6)

• On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition
• Sliced Wasserstein Kernel for Persistence Diagrams
• Learning metrics for persistence-based summaries and applications for graph classification
• Approximating Continuous Functions on Persistence Diagrams Using Template Functions
• Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
• A Study on Topological Descriptors for the Analysis of 3D Surface Texture

Cited by in corpus (8)

• A Look into Chaos Detection through Topological Data Analysis
• Transfer Learning for Autonomous Chatter Detection in Machining
• Topological Data Analysis in smart manufacturing: State of the art and futuredirections
• Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
• Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
• Persistent Homology of Coarse Grained State Space Networks
• ANAPT: Additive Noise Analysis for Persistence Thresholding
• Graph-based Summary Statistics for Revealing the Stochastic Gravitational Wave Background in Pulsar Timing Arrays