用人工神经网络研究了化学成分及热处理工艺参数对低碳低合金钢的硬度的影响.首先设计了RBF型人工神经网络模型,用“舍一法”改进了模型,使其具有较好的预测性能.然后,用神经网络研究了化学成分和冷速对低碳低合金钢的硬度的定量影响.结果表明,碳的质量分数为0.11％～0.15％时,硬度随碳含量的增加而增大；硅的质量分数为0.24％～0.38％、锰的质量分数为0.94％～1.02％时,硬度值基本不变；铬的质量分数为0～0.6％时,硬度值呈增加趋势；镍的质量分数为0～0.04％时,硬度值基本不变；钼的质量分数为0～0.2％时,硬度值从HV288降至HV282；硼的质量分数为1％～2％时,硬度随含量增加而升高；钛、铌、钒的总质量分数为0.06％～0.14％时,硬度值基本不变；冷速从10℃/m增加至170℃/m,硬度值从HV290增至HV420.
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