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Prediction of Burr Types using the Taguchi Method and an Artificial Neural Network  

Lee, Seoung-Hwan (한양대학교 기계공학과)
Kim, Seol-Bim (한양대학교 대학원 메카트로닉스시스템공학과)
Cho, Yong-Won (한양대학교 대학원 메카트로닉스시스템공학과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.15, no.3, 2006 , pp. 45-52 More about this Journal
Abstract
Burrs formed during face milling operations can be very difficult to characterize since there exist several parameters which have complex combined effects that affect the cutting process. Many researchers have attempted to predict burr characteristics including burr size and shape, using various experimental parameters such as cutting speed, feed rate, in-plane exit angle, and number of inserts. However, the results of these studies tend to be limited to a specific process parameter range and to certain materials. In this paper, the Taguchi method, a systematic optimization method for design and analysis of experiments, is introduced to acquire optimum cutting conditions for burr minimization. In addition, an in process monitoring scheme using an artificial neural network is presented for the prediction of burr types.
Keywords
Taguchi method; Neural network; Milling; Burr; Non-dimensionalization;
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