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Mechanistic models for additive manufacturing of metallic components
Progress in Materials Science ( IF 33.6 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.pmatsci.2020.100703
H.L. Wei , T. Mukherjee , W. Zhang , J.S. Zuback , G.L. Knapp , A. De , T. DebRoy

Abstract Additive manufacturing (AM), also known as 3D printing, is gaining wide acceptance in diverse industries for the manufacturing of metallic components. The microstructure and properties of the components vary widely depending on printing process and process parameters, and prediction of causative variables that affect structure, properties and defects is helpful for their control. Since models are most useful when they can correctly predict experimental observations, we focus on the available mechanistic models of AM that have been adequately validated. Specifically, the applications of transport phenomena models in the studies of solidification, residual stresses, distortion, formation of defects and the evolution of microstructure and properties are critically reviewed. The functionality of AM models in understanding of the printability of commonly used AM alloys and the fabrication of functionally graded alloys are also assessed. Opportunities for future research are identified considering the gaps in knowledge in modeling. The uniqueness of this review includes substantive discussions of the rapid certification of the AM components aided by scale models, bidirectional models, cloud based big data, machine learning and digital twins of AM hardware.

中文翻译:

用于金属部件增材制造的机械模型

摘要 增材制造 (AM),也称为 3D 打印,在金属部件制造的各个行业中得到广泛接受。部件的微观结构和性能因印刷工艺和工艺参数而异,预测影响结构、性能和缺陷的原因变量有助于对其进行控制。由于模型在可以正确预测实验观察时最有用,因此我们专注于已充分验证的可用 AM 机械模型。具体而言,对传输现象模型在凝固、残余应力、变形、缺陷形成以及微观结构和性能演变研究中的应用进行了严格审查。还评估了 AM 模型在理解常用 AM 合金的可印刷性和功能梯度合金的制造方面的功能。考虑到建模知识的差距,确定了未来研究的机会。本次审查的独特之处包括对比例模型、双向模型、基于云的大数据、机器学习和 AM 硬件数字孪生辅助的 AM 组件的快速认证的实质性讨论。
更新日期:2021-02-01
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