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최원식,프라타마 판두 산디,수페노 데스티아니,변재영,이은숙,우지희,양지웅,키프 디마스 하리스 신,크리스타 마이난다 브리기타,오케추쿠 나에메카 니콜라스,이강삼,Choi, Wonsik,Pratama, Pandu Sandi,Supeno, Destiani,Byun, Jaeyoung,Lee, Ensuk,Woo, Jihee,Yang, Jiung,Keefe, Dimas The Korean Society of Industry Convergence 2018 한국산업융합학회 논문집 Vol.21 No.5
In this research, the effect of normal load, sliding velocity, and texture density on thefriction coefficient of surfaces micro-textured on AISI 4140 under paraffin oil lubrication were investigated. The predicted tribological behavior by numerical calculation can be serves as guidance for the designer during the machine development stage. Therefore, in this research friction coefficient prediction model based on response surface methodology (RSM), support vector machine (SVM), and artificial neural network (ANN) were developed. The experimental result shows that the variation of load, speed and texture density were influence the friction coefficient. The RSM, ANN and SVM model was successfully developed based on the experimental data. The ANN model can effectively predict the tribological characteristics of micro-textured AISI 4140 in paraffin oil lubrication condition compare to RSM and SVM.
밭농업용 소형 전동운반차의 구동부의 감속기 내구성 향상
박춘숙 ( Cunsook Park ),프라타마판두산디 ( Pandu Sandi Pratama ),수페노데스티아니 ( Destiani Supeno ),정성원 ( Seongwon Jeong ),디마스하리스새얀키피 ( Dimas Haris Sean Keefe ),우지희 ( Jihee Woo ),이은숙 ( Eunsook Lee ),윤우진 ( Woojin Yoon 한국농업기계학회 2017 한국농업기계학회 학술발표논문집 Vol.22 No.1
국내에서 사용되는 밭농업용 전동차는 산업용 DC MOTOR를 사용하고 있는 실정이다 왜냐하면 농업용DC MOTOR는 그 수요가 미미해서 개발상품이 거의 없는 실정이다. 산업용DC MOTOR는 고정된 장치에 부착이 되어 있어 사용상에 별 문제가 없지만 밭농업용 전동차는 비포장 도로에 사용되고 있어 충격이나 과부하에 견디지 못하여 운행도중 심한충격이나 하중 또는 진동에 반복하중에 견디지 못하여 파괴되거나 소음등이 많이 나는경우가 많다. 따라서 본 연구에서는 이를 개선하기 위해 감속기의 재질을 분석하고 마찰 마멸이 일어나는 부분의 재질변경과 표면열처리 등을 통하여 내구성을 증대에 대한연구를 실행 하였다.
Wonsik Choi(최원식),Pandu Sandi Pratama(프라타마 판두 산디),Destiani Supeno(수페노 데스티아니),Jaeyoung Byun(변재영),Ensuk Lee(이은숙),Jihee Woo(우지희),Jiung Yang(양지웅),Dimas Harris Sean Keefe(키프 디마스 하리스 신),Maynanda Brigita Chry 한국산업융합학회 2018 한국산업융합학회 논문집 Vol.21 No.5
In this research, the effect of normal load, sliding velocity, and texture density on thefriction coefficient of surfaces micro-textured on AISI 4140 under paraffin oil lubrication were investigated. The predicted tribological behavior by numerical calculation can be serves as guidance for the designer during the machine development stage. Therefore, in this research friction coefficient prediction model based on response surface methodology (RSM), support vector machine (SVM), and artificial neural network (ANN) were developed. The experimental result shows that the variation of load, speed and texture density were influence the friction coefficient. The RSM, ANN and SVM model was successfully developed based on the experimental data. The ANN model can effectively predict the tribological characteristics of micro-textured AISI 4140 in paraffin oil lubrication condition compare to RSM and SVM.