Neuro evolutionary model for weld residual stress prediction

被引:14
作者
Dhas, John Edwin Raja [1 ]
Kumanan, Somasundaram [2 ]
机构
[1] Noorul Islam Ctr Higher Educ, Dept Automobile Engn, Nagercoil 629180, Tamil Nadu, India
[2] Natl Inst Technol, Dept Prod Engn, Tiruchirappalli 620015, Tamil Nadu, India
关键词
Finite element method; Neural network; Genetic algorithm; Particle swarm optimization; Residual stress; X-ray diffraction;
D O I
10.1016/j.asoc.2013.08.019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Welding is an efficient reliable metal joining process in which the coalescence of metals is achieved by fusion. Localized heating during welding, followed by rapid cooling, induce residual stresses in the weld and in the base metal. Determination of magnitude and distribution of welding residual stresses is essential and important. Data sets from finite element method (FEM) model are used to train the developed neural network model trained with genetic algorithm and particle swarm optimization (NN-GA-PSO model). The performance of the developed NN-GA-PSO model is compared neural network model trained with genetic algorithm (NN-GA) and neural network model trained with particle swarm optimization (NN-PSO) model. Among the developed models, performance of NN-GA-PSO model is superior in terms of computational speed and accuracy. Confirmatory experiments are performed using X-ray diffraction method to confirm the accuracy of the developed models. These developed models can be used to set the initial weld process parameters in shop floor welding environm ent. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:461 / 468
页数:8
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