Data-driven multi-scale multi-physics models to derive process-structure-property relationships for additive manufacturing
Data-driven multi-scale multi-physics models to derive process-structure-property relationships for additive manufacturing
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DOI：
10.1007/s00466-018-1539-z
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发表时间：
2018-05-01
影响因子：
4.1
通讯作者：
Liu, Wing Kam
中科院分区：
文献类型：
--
作者：
Yan, Wentao;Lin, Stephen;Liu, Wing Kam
增材制造（AM）具有吸引人的潜力，可以在不需要专门工具的情况下操纵任意形状的最终用途产品中的材料成分、结构和性能。由于物理过程难以通过实验测量，因此数值模拟是了解潜在物理机制的有力工具。本文介绍了我们在这方面的最新工作，基于AM材料的工艺-结构-性能关系的综合材料建模。增材制造过程中出现的众多影响因素激发了对新型快速设计和优化方法的需求。为此，我们提出数据挖掘作为一种有效的解决方案。这些方法——用于工艺-结构、结构-性能和连接它们的设计阶段——将允许AM加工和材料的设计循环。我们希望这篇文章将提供一个路线图，使AM的监测和AM处理的高级诊断的基本理解。
Additive manufacturing (AM) possesses appealing potential for manipulating material compositions, structures and properties in end-use products with arbitrary shapes without the need for specialized tooling. Since the physical process is difficult to experimentally measure, numerical modeling is a powerful tool to understand the underlying physical mechanisms. This paper presents our latest work in this regard based on comprehensive material modeling of process-structure-property relationships for AM materials. The numerous influencing factors that emerge from the AM process motivate the need for novel rapid design and optimization approaches. For this, we propose data-mining as an effective solution. Such methods-used in the process-structure, structure-properties and the design phase that connects them-would allow for a design loop for AM processing and materials. We hope this article will provide a road map to enable AM fundamental understanding for the monitoring and advanced diagnostics of AM processing.