ABSTRACT

In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.

摘要

在当前的评论中，讨论了在集成计算材料工程 (ICME) 框架下金属材料增材制造 (AM) 中多尺度集成计算建模和数据驱动方法的特殊观点。在评论的第一部分，过程模拟（PS联动）、结构建模（SP联动）、特性模拟（SP联动）和综合建模（PSP和PSPP联动）在考虑不同物理现象（多物理场）的情况下进行了阐述。 AM 和微观/中观/宏观尺度（多尺度建模）。第二部分对数据驱动框架进行了广泛的讨论，其中涉及从数据库和文本中提取现有数据、数据预处理、高通量筛选以及因此的数据库构建。数据驱动的工作流集成了包括 ML、人工智能 (AI) 和神经网络 (NN) 模型在内的统计方法，具有完成 PSPP 链接的巨大潜力。这篇评论论文为从事金属材料增材制造的学术和工业研究人员提供了见解。