  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.
