Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
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DOI：
10.1557/jmr.2020.120
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发表时间：
2020-06
影响因子：
2.7
通讯作者：
Yi Han;R. J. Griffiths;Hang Z. Yu;Yunhui Zhu
中科院分区：
文献类型：
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作者：
Yi Han;R. J. Griffiths;Hang Z. Yu;Yunhui Zhu
金属增材制造（AM）通过过程控制为微观结构优化提供了一个平台，但是建立定量加工-微观结构联系需要有效的微观结构表示和再生方案。在这里，我们提出了一个深度学习框架来定量分析AM在不同工艺条件下制造的金属的微观结构变化。直接从电子背散射衍射图案中提取主要的微观结构描述符，从而能够在减少的代表域中定量测量微观结构差异。我们还展示了使用再生神经网络预测表示域内的新微观结构的能力，从中我们能够通过将再生微观结构映射为主成分值的函数来探索隐式表达的微观结构描述符的物理见解。我们使用固态AM技术，添加剂摩擦搅拌沉积，这通常会导致在等轴显微组织制造的样品的框架的有效性进行验证。
Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.