Machining condition monitoring for micro-grooving on mold steel using fuzzy clustering method

퍼지 클러스터링을 이용한 금형강에 미세 그루브 가공시 가공상태 모니터링

  • 이은상 (인하대학교 기계공학부) ;
  • 곽철훈 (인하대학교 대학원 기계공학과) ;
  • 김남훈 (부산대학교 대학원 정밀기계공학과)
  • Published : 2003.11.01

Abstract

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing process. STD11 has been known as difficult-to-cut materials. The micro-grooving machine was developed for this study and the experiments were performed using CBN blade for machining STD11. Evaluating the machining conditions, frequency spectrum analysis of acoustic emission (AE) signals according to each conditions were applied. Fuzzy clustering method for associating the preprocessor outputs with the appropriate decisions was followed by frequency spectrum analysis. FFT is used to decompose AE signal into different frequency bands in time domain, the root mean square (RMS) values extracted from the decomposed signal of each frequency band were used as features.

Keywords

References

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