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 : Vikrant Guleria (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Vikrant Guleria,Vivek Kumar,Pradeep K. Singh 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.6
The abrupt changes in tool-workpiece interaction during machining process induce variation in the surface quality of work material. These interactions include built-up edge formation and their break-off, environmental conditions (use of coolant, rise of temperature etc.), material imperfections, improper structural fitness of machine & tool components, etc. This study presents prediction of surface roughness in turning of EN353 steel implementing the variational mode decomposition (VMD) for processing the vibration data, followed by estimation of the surface roughness using the relevance vector regression (RVR) optimized by particle swarm optimization (PSO). The raw vibration data has been decomposed in five discrete sets of frequency components known as variational mode functions (VMFs). A set of twenty-one statistical features in each three axes have been extracted for raw data and each VMF. The RVR has been trained using these 21×3 = 63 features and 3 cutting parameters – cutting speed, feed depth of cut. The RVR has also been trained separately using top 5 features selected through RreliefF algorithm. The optimal decomposition level has been determined to minimize the noise and predict the surface finish accurately. The results obtained in 1st VMF (high frequency, low amplitude) using its top 5 features for prediction have been found to be reliable with higher prediction accuracy.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
