Tribological Properties and Friction Coefficient Prediction Model of 200μm Surfaces Micro-Textured on AISI 4140 in Soybean Crusher = 콩 분쇄기의 AISI 4140에서 200μm 미세 패턴 표면의 마찰 계수 및 마찰 계수 예측 모델

다국어 초록 (Multilingual Abstract)

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.