Browse > Article

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

이은상 (인하대학교 기계공학부)
곽철훈 (인하대학교 대학원 기계공학과)
김남훈 (부산대학교 대학원 정밀기계공학과)
Publication Information
Journal of the Korean Society for Precision Engineering / v.20, no.11, 2003 , pp. 47-54 More about this Journal
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
Micro-grooving; AE; Fuzzy clustering; FCM; Acoustic Emission; Fuzzy c-means;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Li, Xiaoli, Yuan, Zhejun, 'Tool wear monitoring with wavelet packet transform-fuzzy clustering method,' wear, PP. 145-154, 1998   DOI   ScienceOn
2 Lim, H.S., Ahn, J.H., 'A study on the cutting conditions of self-induced chattering in micro shaping with diamond tool', Journal of the KSPE, Vol. 15, No. 3, pp. 141-149, 1998   과학기술학회마을
3 Dornfeld, D., Cai, H.G., 'An investigation of grinding of wheel loading using acoustic emission,' Trans. ASME, Vol. 106, pp. 28-33, 1984   DOI
4 Malkin, S., 'Grinding Technology,' Grinding Mechanisms, pp. 107-142, 1988
5 Li, Xiaoli, 'A brief review: acoustic emission method for tool wear monitoring during turning,' International Journal of Machine Tools & Manufacture, Vol. 42, pp. 157-165, 2002   DOI   ScienceOn
6 Lee, K.H., Oh, K.L., 'Fuzzy theory and application II,' Hong-reung, PP. 7-19-7-23, 1992
7 Li, P.G., Wu, S.M., 'Monitoring drill wear states using a fuzzy pattern recognition technique,' Trans. ASME, J. Eng. Ind. 110(2),pp.297-300, 1988