Publication Type:

Journal Article

Source:

International Journal of Applied Engineering Research, Research India Publications, Volume 10, Number 3, p.8401-8416 (2015)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84926500132&partnerID=40&md5=3d61993ba7bfd6b61cccb3335e9416e6

Abstract:

<p>Single point cutting tool (SPCT) is one of the most significant machine tools which has been used in the present industrial era. Tool wear and tool life are the principle areas to be focused on. This paper manifests the condition monitoring which was done on SPCT in the interest of perfect surface finish. Closer and effective observations were made while in operation. The developed failure in the form of vibration signal had been revealed. From the vibration signals, ARMA features were extracted. The extracted features were then classified by using a supervised learning model called Support Vector Machine (SVM). A case study has been done for various types and range of problems in this particular tool, in a cross reference with the extracted feature set. The obtained results were compared. Unscheduled outages, machine performance optimization, repair time reduction and maintenance cost can be avoided with the help of this paper. © Research India Publications.</p>

Notes:

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Cite this Research Publication

K. Sa Shalet, Sugumaran, Va, Jegadeeshwaran, Ra, and M. Elangovan, “Condition monitoring of single point cutting tool using arma features and SVM classifiers”, International Journal of Applied Engineering Research, vol. 10, pp. 8401-8416, 2015.