کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1654475 | 1007695 | 2005 | 6 صفحه PDF | دانلود رایگان |

An intelligent technique of artificial neural networks combined with genetic algorithms is developed for the analysis and optimization of the correlation between heat treatment parameters and properties in Cu–Cr–Sn–Zn lead frame alloy. A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of aging treatment with respect to hardness and conductivity properties was proposed for the alloy. The ANN sub-model improved by the Levenberg–Marquardt training algorithm has good generalization performance. Genetic algorithms (GAs) are used to optimize the input parameters of aging temperature and time. The verifying experiment has shown that the theoretical optimization agrees with the experimental evidence.
Journal: Materials Letters - Volume 59, Issue 26, November 2005, Pages 3337–3342