通过深度学习和多尺度数据挖掘相结合预测钢的马氏体起始温度

IF 3.1 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Shuai Wang, Xunwei Zuo, Nailu Chen, Yonghua Rong
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引用次数: 0

摘要

马氏体起始(Ms)温度在指导钢的合金设计和热处理过程中起着重要作用。然而,由于马氏体转变的复杂性,建立更通用的模型仍具有挑战性。在本研究中,首先不依赖热力学软件,通过多尺度数据挖掘建立了三个尺度的数据库。然后,利用多尺度数据库训练了一个卷积神经网络(CNN)模型和四个传统机器学习(ML)模型来预测 Ms。CNN 模型的误差最小,五个模型的表现均优于仅通过合金成分训练的模型,这表明了特征多样性的好处。基准测试表明,与经验方程、JMatPro 软件和热力学模型相比,CNN 模型具有更高的精度。此外,在 "三阶段特征筛选 "的每个阶段后,用剩余特征训练的简化 CNN 模型与原始 CNN 模型的误差都只有约 1 K,这说明了当前特征筛选策略的有效性和 CNN 模型的良好鲁棒性。此外,CNN 模型还可用于预测未知成分组合合金的 Ms,从而揭示合金元素对奥氏体稳定性和合金设计的影响。将多尺度数据挖掘整合到以 CNN 为代表的深度学习框架中,为预测与合金成分存在复杂关系的某些属性提供了一种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting the martensite start temperature of steels via a combination of deep learning and multi-scale data mining

Predicting the martensite start temperature of steels via a combination of deep learning and multi-scale data mining
The martensite start (Ms) temperature, plays a significant role in guiding the alloy design and heat treatment process for steels. However, due to the complexity of martensite transformation, it remains challenging to establish more generalized models. In this study, the database with three scales was first built by multi-scale data mining, without relying on thermodynamic software. Then a convolutional neural network (CNN) model, as well as four traditional machine learning (ML) models, were trained to predict Ms using multi-scale database. The CNN model exhibits the smallest error, and the five models all perform better than those trained solely by alloy composition, demonstrating the benefits of feature diversity. The benchmarking test indicates that the CNN model has higher accuracy, compared to the empirical equations, JMatPro software, and thermodynamic model. Besides, the simplified CNN models trained with remaining features after each stage of the ‘three-stage feature screening’ all show an error of only about 1 K from the original CNN model, illustrating the effectiveness of the current feature screening strategy and good robustness of the CNN model. Moreover, the CNN model can be utilized to predict the Ms of the alloys with unknown compositional combinations, then new insights about the impacts of alloy elements on austenite stability and alloy design can be revealed. The integration of multi-scale data mining into a deep learning framework represented by CNN, offers a recipe for predicting certain attributes that are involved in complicated relationships with alloy composition.
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来源期刊
Computational Materials Science
Computational Materials Science 工程技术-材料科学:综合
CiteScore
6.50
自引率
6.10%
发文量
665
审稿时长
26 days
期刊介绍: The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.
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