Abstract
In this paper, an intelligent tool breakage detection system which uses a support vector machine (SVM) learning algorithm is proposed to provide the ability to recognize process abnormalities and initiate corrective action during a manufacturing process, specifically in a milling process. The system utilizes multiple sensors to record cutting forces and power consumptions. Attention is focused on training the proposed system for performance improvement and detecting tool breakage. Performance of the developed system is compared to the results from an alternative detection system based on a multiple linear regression model. It is expected that the proposed system will reduce machine downtime, which in turn will lead to reduced production costs and increased customer satisfaction.
Original language | English (US) |
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Pages (from-to) | 241-249 |
Number of pages | 9 |
Journal | International Journal of Machine Tools and Manufacture |
Volume | 45 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2005 |
Keywords
- Multiple sensors
- Support vector machine
- Tool breakage detection
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Mechanical Engineering