Accurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments and machine learning on steels for low-temperature service. An algorithm based on a convolutional neural network enriched with the two-dimensional input terminals increased the prediction accuracy, achieving an average microhardness error of as low as 14.37 HV for the validation set. The improved prediction accuracy is ascribed to the comprehensive strengthening mechanism and coupling of strengthening effects contained in the multifarious input terminals. The information acquisition and cross-correlation of substructures related to strengthening mechanism played an important role. The reported strategy can deepen the cognition of the strengthening mechanism of tempered martensite. It is promising for application to different steels containing tempered martensite.

通过使用基于传统强化机制的模型准确预测含马氏体钢的性能仍然是一个挑战。在这项研究中，使用高通量实验和机器学习对低温服务钢进行了开发，开发了一种具有二维微观结构输入终端的智能机器学习模型。基于二维输入终端丰富的卷积神经网络的算法提高了预测精度，验证集的平均显微硬度误差低至 14.37 HV。预测精度的提高归因于各种输入终端所包含的综合强化机制和强化效果的耦合。与加强机制相关的子结构的信息获取和互相关发挥了重要作用。所报告的策略可以加深对回火马氏体强化机制的认识。它有望应用于含有回火马氏体的不同钢种。