Analysis of workability behavior of Al-SiC P/M composites using backpropagation neural network model and statistical technique

被引:30
|
作者
Sivasankaran, S. [1 ]
Narayanasamy, R. [1 ]
Ramesh, T. [2 ]
Prabhakar, M. [2 ]
机构
[1] Natl Inst Technol, Dept Prod Engn, Thiruchirappalli 620015, India
[2] Natl Inst Technol, Dept Mech Engn, Thiruchirappalli 620012, India
关键词
Metal matrix composites; Powder metallurgy; Artificial neural network; Analysis of variance; POWDER-METALLURGY COMPOSITE; MATRIX COMPOSITES; MECHANICAL-PROPERTIES; PROCESS PARAMETERS; PARTICLE-SIZE; TOOL-WEAR; PREDICTION; FRACTURE; ALLOY; SCIENCE;
D O I
10.1016/j.commatsci.2009.06.013
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents an artificial neural network (ANN) model for predicting and analyzing the workability behavior during cold upsetting of sintered Al-SiC powder metallurgy (P/M) metal matrix composites (MMCs) under triaxial stress state condition which is the multifaceted technological concept, depending upon the ductility of the material and the process parameters. The input parameters of the ANN model are the preform density, the particle size, the percentage of reinforcement and the applied load. The output parameters of the model are the axial stress, the hoop stress, the axial strain, the hoop strain, the instantaneous strain hardening index, and the instantaneous strength coefficient. This model is a feed forward backpropagation neural network and is trained and tested with pairs of input/output data. A very good performance of the neural network, in terms of good agreement with the experimental data has been achieved. As a secondary objective, quantitative and statistical analyses were performed in order to evaluate the effect of the process parameters on the workability and the plastic deformation behavior of the composites. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:46 / 59
页数:14
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