为实现普碳钢中厚板的柔性化轧制, 建立了一种新的用于制定温度制度的理论算法. 首先建立最佳预测能力的人工神经网络, 用于预测中厚板力学性能; 然后运用遗传算法制定出温度制度, 并由回归出的力学性能公式对预测结果进行验证. 结果表明, 通过回归法和人工神经网络均可精确地预测中厚板的力学性能, 而且神经网络的预测精度比回归公式的预测精度高; 终轧温度和终冷温度对力学性能的影响最大; 通过温度制度和力学性能回归公式计算出的强度, 与目标强度非常吻合; 对同一成分的钢种, 通过制定合适的温度制度可以轧制出不同强度的产品, 以减缓中厚板产品大规模定制中各阶段之间的矛盾.
A new mathematical model constituting temperature schedule was established in order to realize flexible rolling of heavy and medium plate, in which the architecture of artificial neuron network (ANN) with optimal ability to predict mechanical properties was first constituted, and then, temperature schedule was constituted by genetic algorithm (GA). It is shown that mechanical properties can be well-predicted by recursive functions and ANN, and the precision predicted by ANN is higher than that by recursive functions. Finishing temperature and final cooling temperature among schedule ones are most important factors for mechanical properties. Mechanical properties calculated by temperature schedule and recursive mechanical-property functions agreed well with desired results. The temperature schedule constituted can be used to produce some steel plates with the same compositions but different strengths.
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