Journal of the Korean Society of Industry Convergence (한국산업융합학회 논문집)

The Korean Society of Industry Convergence (한국산업융합학회)
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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

  • Choi, Wonsik (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Pratama, Pandu Sandi (Life and Industry Convergence Research Institute Pusan National University) ;
  • Supeno, Destiani (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Byun, Jaeyoung (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Lee, Ensuk (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Woo, Jihee (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Yang, Jiung (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Keefe, Dimas Harris Sean (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Chrysta, Maynanda Brigita (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Okechukwu, Nicholas Nnaemeka (Department of Bio-industrial Machinery Engineering Pusan National University) ;
  • Lee, Kangsam (Slow food corporation)
  • Received : 2018.08.29
  • Accepted : 2018.09.14
  • Published : 2018.09.30
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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.

Keywords

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Fig. 1 Hexagonal pattern array observed under scanning electron microscope (SEM).

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Fig. 2 Scanning electron microscope dimple pin density before experiment (a) 10%, (b) 12.5%, (c) 15%, and (d) 20%

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Fig. 3 Structure of ANN process simulation

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Fig. 4 Friction coefficients as a function of velocity for 10% density of micro dimple.

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Fig. 5 Friction coefficients as a function of velocity for 10% density of micro dimple.

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Fig. 6 Friction coefficients as a function of texture density of micro dimple.

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Fig. 7 SEM image of dimpled pin dimple condition after test.

Table 1. Friction coefficient from experiment

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Table 2. Friction coefficient from experiment

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Table 3. Comparison of mathematical models

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