Machine Learning Applied to Acoustic Emission tor Tool Wear Classification during Milling of Composite Materials

In the last decades, the use of composite materials was increased in aerospace and aeronautical industries. The tolerances of manufactured components are decisive in its acceptance. In this way, tool monitoring is an instrument to control the precision of the cutting processes. The drilling/milling...

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Bibliographic Details
Published inJournal of acoustic emission Vol. 37; p. S11
Main Authors A.G., Bonelli Toro, M.P., Gomez
Format Journal Article
LanguageEnglish
Published Acoustic Emission Group 01.01.2020
Subjects
Acoustic emission testing
Aerospace industry
Algorithms
Composite materials
Data mining
Machine learning
Machining
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Summary:In the last decades, the use of composite materials was increased in aerospace and aeronautical industries. The tolerances of manufactured components are decisive in its acceptance. In this way, tool monitoring is an instrument to control the precision of the cutting processes. The drilling/milling process involves a 60% of the rejections of manufactured composite components. Tool wear is one of the main reasons for faulty milling as it achieves a poor termination and inaccurate size of the holes. In the present work, milling tools used in machining (milling or drilling) of sandwich structure panels for aerospace applications were studied for different wear stages. Elastic waves generated during the cutting action were measured by the method of acoustic emission. Three tools with different degree of wear were selected and different machine learning algorithms were implemented PCA and t-SNE for dimensionality reduction and random forest and Kohonen maps to identify the tool condition. The first results are encouraging to carry on these applications to online tool wear classification. Keywords: acoustic emission, machining, composite materials, machine learning
ISSN:0730-0050