Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

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 fea...

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Vydané v:arXiv.org
Hlavní autori: Yesilli, Melih C, Tymochko, Sarah, Khasawneh, Firas A, Munch, Elizabeth
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 27.10.2019
Predmet:
Algorithms
Artificial intelligence
Chatter
Computer simulation
Cutting parameters
Cutting tools
Machine learning
Machine tool industry
Machine tools
Milling (machining)
Signal processing
Supervised learning
Surface finish
Tool life
Topology
Vibration
ISSN:2331-8422
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Popis
Shrnutí: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.
Bibliografia:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1910.12359