In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material structures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these structures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target structures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable structure. The functionality of the approach will be demonstrated manufacturing crystallographic textures with desired properties in a metal forming process.

翻译：近年来，在工艺-结构-性能链条的背景下，加速材料创新的兴趣日益增长。为此，必须考虑制造工艺，并定制材料设计方法以支持下游工艺设计方法。作为迈向此方向的重要一步，我们提出了一种涵盖材料工程中整个工艺-结构-性能链条的整体优化方法。我们的方法专门利用机器学习来解决两个关键的识别问题：一个材料设计问题，涉及识别具有所需性能的接近最优的材料结构；以及一个工艺设计问题，旨在找到制造这些结构的最佳加工路径。这两个识别问题通常都是不适定的，这对求解方法构成了重大挑战。然而，这些问题的非唯一性为加工提供了一个重要优势：通过拥有多个性能相似的目标结构，可以有效地引导工艺制造出最佳可达结构。该方法的功能将通过在一个金属成形工艺中制造具有所需性能的晶体学织构来得到演示。