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Prediction of Burr Size in Micro-drilling  

이성환 (한양대학교 기계공학과)
권성용 (한양대학교 대학원 정밀기계공학과)
Publication Information
Journal of the Korean Society for Precision Engineering / v.20, no.11, 2003 , pp. 71-78 More about this Journal
Abstract
The exit burrs in the micro-drilling of precision miniature holes are of interest, especially for ductile materials. As burrs from this process can be difficult to remove, it is important to acquire the way of predicting burr types as well as optimal cutting conditions which minimize the burrs. In this paper, an artificial neural network was used for the prediction of burr formation in micro-drilling. First, the influence of cutting conditions including cutting speed, feed and drill diameter on the exit burr characteristics, such as burr size and type, were observed and analyzed. Then. the burr types were classified by using the influential experimental data as input parameters to the neural nets.
Keywords
Micro-drilling; exit burr; deburring; burr type; cutting condition; artificial neural network;
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Times Cited By KSCI : 1  (Citation Analysis)
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