根据收集和整理的实验数据,建立了低碳低合金钢的成分与马氏体转变开始温度(M以点)的反向传播(BP)人工神经网络,用这种方法预测了一些钢的Ms点,并与用其它经验公式得到的结果进行了比较.结果表明:用人工神经网络能更精确地预测钢的Ms点,预测精度明显高于其它线性经验公式.另外用正交实验法设计了几种基准成分的钢,用人工神经网络分析了几种合金元素对Ms点的定量影响,计算结果表明,与传统的经验公式表达的信息不同,合金元素的含量与钢的Ms点间表现为非线性关系.可以认为,这种非线性关系是由合金元素间复杂的交互作用引起的.
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