Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis

Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been p...

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Bibliographic Details
Published inInternational journal of prognostics and health management Vol. 8; no. 2
Main Authors Bohara, Navneet, R, Jegadeeshwaran, G, Sakthivel
Format Journal Article
LanguageEnglish
Published The Prognostics and Health Management Society 16.11.2020
Subjects
carbide inserts
confusion matrix
machine learning
statistical features
vibration analysis
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Summary:Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.
ISSN:2153-2648
2153-2648
DOI:10.36001/ijphm.2017.v8i2.2635