Sivarao, Subramonian (2004) Comparison of statistical and mathematical methods for determination of tool wear in drilling. Journal of Mechanical Engineering, 55 (4). pp. 187-198. ISSN 0039-2472
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Abstract
Metal removing process is becoming increasingly more complex, demanding, and it experiences an unprecedented growth. It has been reported that one-third of material removal process performed in industry is drilling operat ion. This complex cutting operation holds a substantial portion of all metal cutting operations, and the largest amount of money spent on any one class of cutting tool is drill. From the viewpoint of cost and productivity, modelling and optimization of drilling process is extremely important for the manufacturing industry. For a successful progress, it is necessary to develop, support and maintain a vigorous innovative manufacturing industry, based on advanced technologies which can operate successfully in the competitive world. The aim of the present work is to identify suitable parameters, monitoring of which enables predictions of drill failures. The paper deals with a comparative performance analysis and modelling of the twist drills at different cutting conditions for a model designed according to factorial design method. Experiments were performed using two straight shank twist drills of different make (tool X and tool Y) possessing the same specifications. The variables selected for tool wear are cutting speed, feed, thrust force, and machining time. The relative comparison of tool wear for tool X and tool Y is done by actual measurement, statistical analysis and inverse coefficient matrix method.
Item Type: | Article |
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Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Faculty of Manufacturing Engineering > Department of Manufacturing Process |
Depositing User: | Assoc. Pror. Ir. Dr. Sivarao Subramonian |
Date Deposited: | 13 Aug 2013 15:43 |
Last Modified: | 28 May 2015 04:02 |
URI: | http://eprints.utem.edu.my/id/eprint/9238 |
Statistic Details: | View Download Statistic |
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