arXiv 2005.052352020-05-11physics.app-phcond-mat.mtrl-sciphysics.comp-ph

金属增材制造中材料开发的机器学习

Machine Learning for Materials Developments in Metals Additive Manufacturing

N. S. Johnson, P. S. Vulimiri, A. C. To, X. Zhang, C. A. Brice, B. B. Kappes, A. P. Stebner

备注：: 42 pages, 14 figures, 4 tables

摘要：

在金属增材制造（AM）中，材料和组件在单个过程中同时制造，因为金属层在最终用途产品所需的接近最终拓扑结构中彼此叠加制造。因此，必须同时控制和理解数十到数百个材料和零件设计自由度;因此，金属AM是一种高度跨学科的技术，需要同步考虑物理，化学，材料科学，物理冶金，计算机科学，电气工程和机械工程。使用现代机器学习方法对这些自由度进行建模可以减少阐明金属AM科学的时间和成本，并优化这些复杂的多学科过程的工程。大多数金属增材制造开发并不需要新的机器学习技术;那些用于材料科学其他领域的技术也适用于增材制造。最多产的是，自21世纪初以来，密度泛函理论（DFT）社区已经使用了其中的许多方法来评估元素和晶体结构的许多组合，以发现新材料。这篇以材料技术为重点的综述通过金属增材制造的视角介绍了机器学习的基本数学和术语，然后探讨了机器学习在推进金属增材制造方面的潜在用途，强调了与材料科学和制造业先前努力的许多相似之处，同时还讨论了金属增材制造特有的新挑战和适应性。

英文摘要：

In metals additive manufacturing (AM), materials and components are concurrently made in a single process as layers of metal are fabricated on top of each other in the near-final topology required for the end-use product. Consequently, tens to hundreds of materials and part design degrees of freedom must be simultaneously controlled and understood; hence, metals AM is a highly interdisciplinary technology that requires synchronized consideration of physics, chemistry, materials science, physical metallurgy, computer science, electrical engineering, and mechanical engineering. The use of modern machine learning approaches to model these degrees of freedom can reduce the time and cost to elucidate the science of metals AM and to optimize the engineering of these complex, multidisciplinary processes. New machine learning techniques are not needed for most metals AM development; those used in other sects of materials science will also work for AM. Most prolifically, the density functional theory (DFT) community has used many of them since the early 2000s for evaluating numerous combinations of elements and crystal structures to discover new materials. This materials technologies-focused review introduces the basic mathematics and terminology of machine learning through the lens of metals AM, and then examines potential uses of machine learning to advance metals AM, highlighting the many parallels to previous efforts in materials science and manufacturing while also discussing new challenges and adaptations specific to metals AM.

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