http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Khan M. Aqib (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Yinfei Yang,Bijun Hao,Xiuqing Hao,Liang Li,Ni Chen,Tao Xu,Khan M. Aqib,Ning He 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.11
Tool condition monitoring is deemed as an essential technology of the intelligent manufacturing. Tool wear which directly affects the tool life makes a negative influence on the quality and dimensional accuracy of the machined surface, even leads to tool breakage, machine downtime, and other severe problems. Therefore, an available tool condition monitoring system is essential for the machining process to guarantee the processing quality and improve the machining efficiency. This paper proposes a new tool condition monitoring method based on the general judgment of cutting force. Milling force from a single-flute is predicted by deducing a theoretical formula based on un-deformed chip thickness. Based on the formula, cutting force samples used for machine learning paradigms are generated through time domain translation and Gaussian distribution. Nonlinear manifold learning methods are applied in the visualization of high dimensional data. Principal component analysis as a practical feature extraction method is used to reduce the large dimensionality of the sample set. The performance of respectively linear kernel, polynomial kernel, radial basis function and sigmoid kernel are self-compared to estimate the classification results via support vector machine. Experiments are carried out on an annealed Ti–6Al–4V alloy to measure the feasibility of this method.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
