Transactions of the Korean Society of Machine Tool Engineers (한국공작기계학회논문집)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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Prediction of Burr Types using the Taguchi Method and an Artificial Neural Network

실험계획법과 뉴럴 네트워크를 이용한 밀링 버 형상 예측

  • 이성환 (한양대학교 기계공학과) ;
  • 김설빔 (한양대학교 대학원 메카트로닉스시스템공학과) ;
  • 조용원 (한양대학교 대학원 메카트로닉스시스템공학과)
  • Published : 2006.06.01
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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.

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References

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