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ERIC Number: EJ905154
Record Type: Journal
Publication Date: 2004
Pages: 7
Abstractor: ERIC
Reference Count: 14
ISBN: N/A
ISSN: ISSN-1071-6084
A Multiple-Regression Model for Monitoring Tool Wear with a Dynamometer in Milling Operations
Chen, Jacob C.; Chen, Joseph C.
Journal of Technology Studies, v30 n4 p71-77 Fall 2004
A major goal of the manufacturing industry is increasing product quality. The quality of a product is strongly associated with the condition of the cutting tool that produced it. Catching poor tool conditions early in the production will help reduce defects. However, with current CNC technology, manufacturers still rely mainly on the operator's experience to operate and monitor machines to avoid defects from poor tool conditions. Since operator experience can be unreliable, recent research has focused on integrating a tool condition monitoring system within the machine to allow online, real-time monitoring to reduce the dependence on human judgment. The purpose of this study was to develop an in-process tool wear monitoring (ITWM) system using cutting force as a sensing signal and integrating the multiple regression approach as the decision mechanism. In order to develop the proposed ITWM system, the following two research outcomes were expected: (1) Identify the cutting force representation that could best predict tool wear; and (2) Build and test an in-process tool wear prediction system, which was a multiple-regression model in this study, with the cutting force identified from the first task. Conclusions of this study are presented. (Contains 6 figures.)
Epsilon Pi Tau. International Office, Technology Building, Bowling Green State University, Bowling Green, OH 43403-0296. Tel: 419-372-2425; Fax: 419-372-9502; e-mail: ept@bgsu.edu; Web site: http://eptglobal.org
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A