Tribilogical performances of connecting rod and by using orthogonal experiment, regression method and response surface methodology

• Published in: Applied soft computing 2015-04, Vol.29, p.436-449
• Main Authors: He, Zhenpeng, Sun, Yigang, Zhang, Guichang, Hong, Zhenyu, Xie, Weisong, Lu, Xin, Zhang, Junhong
• Format: Article
• Language: English
• Subjects: Connecting rod; Dynamic lubrication; Orthogonal experiment; Stepwise regression; Support vector machine (SVM)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.01.009

•Different from tradition analysis method, the statistics method with suitable design of experiment is used to gain more information.•The identification of the factors dominating the bearing behaviors is obtained.•The new regression models without insignificant components are established through the stepwise regression.•The SVM model and POS-SVM model are established to identify the asperity contact. Dynamic lubrication analysis of connecting rod is a very complex problem. Some factors have great effect on lubrication, such as clearance, oil viscosity, oil supplying hole, bearing elastic modulus, surface roughness, oil supplying pressure and engine speed and bearing width. In this paper, ten indexes are used as the input parameters to evaluate the bearing performances: minimum oil film thickness (MOFT), friction loss, the maximum oil film pressure (MOFP) and average of the oil leakages (OLK). Two orthogonal experiments are combined to identify the factors dominating the bearing behavior. The stepwise regression is used to establish the regression model without insignificant variables, and two most important variables are used as the input to carry out the surface response analysis for each model. At last, the support vector machine (SVM) is used to identify the asperity contact. Compared with SVM model, the particle swarm optimization-support vector machines (PSO–SVM) can predict the asperity contact more precise, especially to the samples near dividing line. In future work, more soft computing methods with statistical characteristic are used to the tribology analyses.