| contributor author | Gao, Xinlei | |
| contributor author | Wang, Ruitao | |
| contributor author | Wang, Zhan | |
| contributor author | Dai, Kang | |
| date accessioned | 2017-05-09T01:33:48Z | |
| date available | 2017-05-09T01:33:48Z | |
| date issued | 2016 | |
| identifier issn | 0742-4787 | |
| identifier other | trib_138_03_031801.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/162672 | |
| description abstract | A series of ball–disk contact friction tests were carried out using a microtribometer to study the tribological characteristics of steel/steel rubbing pairs immersed in 47 different organic compounds as lubricant base oils. The structures and their friction data were included in a backpropagation neural network (BPNN) quantitative structure triboability relationship (QSTR) model. Following leaveoneout (LOO) crossvalidation, the BPNN model shows good predictability and accuracy for the friction parameter (R2 = 0.994, R2(LOO) = 0.849, and q2 = 0.935). Connectivity indices (CHI) show the large positive contribution to friction, which imply that friction performance has a strong correlation with molecular structure. The BPNN–QSTR models can flexibly and easily estimate the friction properties of lubricant base oils. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | BPNN–QSTR Friction Model for Organic Compounds as Potential Lubricant Base Oils | |
| type | Journal Paper | |
| journal volume | 138 | |
| journal issue | 3 | |
| journal title | Journal of Tribology | |
| identifier doi | 10.1115/1.4032304 | |
| journal fristpage | 31801 | |
| journal lastpage | 31801 | |
| identifier eissn | 1528-8897 | |
| tree | Journal of Tribology:;2016:;volume( 138 ):;issue: 003 | |
| contenttype | Fulltext | |
