DocumentCode
527618
Title
Notice of Retraction
Superplasticity prediction and application of albronze based on artificial neural network
Author
Guo Junqing ; Chen Fuxiao ; Yang Yongshun ; Li Hejun
Author_Institution
Coll. of Mater. Sci. & Eng., Henan Univ. of Sci. & Technol., Luoyang, China
Volume
2
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
668
Lastpage
670
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.
The superplastic performances prediction of albronze based on artificial neural network was studied in this paper. Used Levenberg-Marquardt algorithm, the predication model of BP neural network which reflects the relationship between the superplastic performances and tension conditions was founded. The superplasticity and optimized condition of albronze were forecasted and the superplastic extrusion tests of solid cage was produced also. The results showed that the error of tests data and prediction was less than 8.5%. It was indicated that the prediction of albronze superplasticity used artificial neural network was effective and feasible.
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.
The superplastic performances prediction of albronze based on artificial neural network was studied in this paper. Used Levenberg-Marquardt algorithm, the predication model of BP neural network which reflects the relationship between the superplastic performances and tension conditions was founded. The superplasticity and optimized condition of albronze were forecasted and the superplastic extrusion tests of solid cage was produced also. The results showed that the error of tests data and prediction was less than 8.5%. It was indicated that the prediction of albronze superplasticity used artificial neural network was effective and feasible.
Keywords
aluminium alloys; backpropagation; copper alloys; extrusion; materials testing; mechanical testing; neural nets; superplasticity; tin alloys; BP neural network; CuSnAl; Levenberg-Marquardt algorithm; albronze; artificial neural network; solid cage; superplastic extrusion test; superplasticity prediction; tension; Aluminum; Artificial neural networks; Biological system modeling; Materials; Strain; Stress; Albronze; Artificial neural network; Prediction model; Superplastic extrusion; Superplasticity;
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.2010.5583350
Filename
5583350
Link To Document