Y.Y.Yang
,
M.Mahfouf
,
D.A.Linkens
材料科学技术（英文）
The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rolling torque, etc., depends on the description of stress-strain relationship of the deformed materials. One common method of describing the stress-strain relationship is using constitutive equations, with the unknown parameters fitted by experimental data obtained via plane strain compression (PSC). Due to the highly nonlinear behaviour of the constitutive equations and the noise included in the PSC data, determination of the model parameters is difficult. In this paper, genetic algorithms were exploited to optimise parameters for the constitutive equations based on the PSC data. The original PSC data were processed to generate the stress-strain data, and data pre-processing was carried out to remove the noise contained in the original PSC data. Several genetic optimisation schemes have been investigated, with different coding schemes and different genetic operators for selection, crossover and mutation. It was found that the real value coded genetic algorithms converged much faster and were more efficient for the parameter optimisation problem.
关键词:
Genetic algorithms
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null
,
null
,
null
,
null
R.Karthikeyan
,
R.Adalarasan
,
B.C.Pai
材料科学技术（英文）
The present work is focused on optimization of machining characteristics of Al/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) were used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.
关键词:
Al/SiCp composites
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null
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null
