DocumentCode
527496
Title
Notice of Retraction
A tribological ANN-model of triphenyl phosphate (TPP) acting on rapeseed oil
Author
Hui Liu ; Jingyu Liu ; Shuo Zhang ; Jinjian Xiao
Author_Institution
Dept. of Vehicle Eng., Chang´ an Univ., Xi´an, China
Volume
2
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1062
Lastpage
1065
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A tribological behavior model of triphenyl phosphate (TPP) acted on rapeseed oil was established by BP-ANN. In the model, a non-linear mapping was built to express the relationships between the load, tribological time, additive content and wear scar diameter. Through training the model, its results showed that the model outputs were identical to the goal outputs, and the precision of the model was higher. By the model simulating, the outputs indicated the antifriction property of rapeseed oil was better when the additive content of TPP was about 1.0%. The tribological behavior of TPP acted on rapeseed oil can be reflected accurately by this model. By forecasting tribological property, the anti-wear mechanism of TPP has been studied on.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A tribological behavior model of triphenyl phosphate (TPP) acted on rapeseed oil was established by BP-ANN. In the model, a non-linear mapping was built to express the relationships between the load, tribological time, additive content and wear scar diameter. Through training the model, its results showed that the model outputs were identical to the goal outputs, and the precision of the model was higher. By the model simulating, the outputs indicated the antifriction property of rapeseed oil was better when the additive content of TPP was about 1.0%. The tribological behavior of TPP acted on rapeseed oil can be reflected accurately by this model. By forecasting tribological property, the anti-wear mechanism of TPP has been studied on.
Keywords
backpropagation; lubricating oils; mechanical engineering computing; neural nets; vegetable oils; wear; BP-ANN; anti-wear mechanism; antifriction property; nonlinear mapping; rapeseed oil; tribological ANN-model; tribological property forecasting; triphenyl phosphate; Additives; Friction; Load modeling; Metals; Neurons; Petroleum; Training; BP-ANN; rapeseed oil; tribology; triphenyl phosphate (TPP);
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5582988
Filename
5582988
Link To Document