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采用BP神经网络建立了关于碳含量对Fe-Cu-C合金性能的关系模型,研究并分析了含碳量和烧结温度对Fe-Cu-C合金性能的影响规律。结果表明:碳含量的增加会使合金内部的渗碳体和铁素体相互转化,烧结温度会严重影响晶粒大小和组织的分布。构建的BP神经网络能够很好的映射各参数对Fe-Cu-C合金性能的关系,预测精度高,计算稳定,具有良好的可靠性和推广意义。
The relationship between carbon content and the properties of Fe-Cu-C alloy was established by BP neural network. The influence of carbon content and sintering temperature on the properties of Fe-Cu-C alloy was studied and analyzed. The results show that the increase of carbon content causes the cementite and ferrite in the alloy to transform into each other, and the sintering temperature will seriously affect the grain size and the distribution of the microstructure. The BP neural network constructed can well map the relationship between the parameters of the properties of Fe-Cu-C alloy, with high prediction accuracy, stable calculation, good reliability and promotion significance.