Experimental Study for the Health Monitoring of Milling Tool Using Statistical Features View Full Text


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Chapter Info

DATE

2021-01-14

AUTHORS

Akanksha Chaudhari , Pavan K. Kankar , Girish C. Verma

ABSTRACT

In this manuscript, the technique for health monitoring of the milling tool has been proposed using statistical features extraction from the raw time domain signal and their trend analysis. The extracted features like mean, kurtosis, skewness are a good indicator of the tool health. An experimental study has been performed in order to obtain the vibration signal using an accelerometer. The surface roughness parameters have been measured using mobile surface measuring instrument “Handy surf”. The surface topography has also been performed for the milled surface with the three different conditions of the tool, i.e. healthy, tending to failure, and the blunt tool. The obtained results show good agreement with the statistical trend analysis. More... »

PAGES

1189-1198

Book

TITLE

Advances in Manufacturing and Industrial Engineering

ISBN

978-981-15-8541-8
978-981-15-8542-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-15-8542-5_106

DOI

http://dx.doi.org/10.1007/978-981-15-8542-5_106

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1134549988


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