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.

金属添加剂制造（AM）提供了一种用于微观组织优化的平台经由过程控制，但是建立定量处理-微结构链接需要一种有效的方案来表示和再生微结构。在这里，我们提出了一个深度学习框架，以定量分析AM在不同加工条件下制造的金属的微观结构变化。直接从电子反向散射衍射图样中提取出主要的微观结构描述子，从而可以在缩小的表示域中定量测量微观结构的差异。我们还演示了使用再生神经网络预测表示域内新微观结构的能力，通过将再生的微结构映射为主要成分值的函数，我们可以从中探索对隐式表达的微结构描述符的物理见解。我们使用固态AM技术制造的样品（添加摩擦搅拌沉积法）来验证框架的有效性，该方法通常会产生等轴的微结构。