Machine learning for structure-guided materials and process design
Lukas Morand Tarek Iraki Johannes Dornheim Stefan Sandfeld Norbert Link Dirk Helm
In recent years, there has been a growing interest in accelerated materials
innovation in both, research and industry. However, to truly add value to the
development of new advanced materials, it is inevitable to take into account
manufacturing processes and thereby 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
materials process-structure-property chain. Our approach specifically employs
machine learning techniques to address two critical identification problems.
The first is to solve a materials design problem, which involves identifying
near-optimal material structures that exhibit desired macroscopic properties.
The second is to solve a process design problem that is to find an optimal
processing path to manufacture these material structures. Both identification
problems are typically ill-posed, which presents a significant challenge for
solution approaches. However, the non-unique nature of these problems also
offers an important advantage for processing: By having several target
structures that perform similarly well, the corresponding processes can be
efficiently guided towards manufacturing the best reachable structure. In
particular, we apply deep reinforcement learning for process design in
combination with a multi-task learning-based optimization approach for
materials design. The functionality of the approach will be demonstrated by
using it to manufacture crystallographic textures with desired properties in a
metal forming process.