The paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives.

本文提出了一种设计钢的化学成分的方法，该方法基于神经网络和遗传算法，旨在实现可能与钢生产相关的其他约束条件相匹配的所需淬透性行为。淬透性是钢的一种机械特性，与广泛的钢应用密切相关，是指钢在热处理后提高硬度的能力。在所提出的方法中，利用了基于神经网络的所谓 Jominy 淬透性轮廓的预测器，并制定了优化问题，其中优化函数允许同时考虑满足目标 Jominy 轮廓的所需精度和其他约束。优化是通过遗传算法进行的。