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Found 5 papers, 2 papers with code
Machine learning for structure-guided materials and process design
The second is to solve a process design problem that is to find an optimal processing path to manufacture these material structures.
A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science.
Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization
Our proposed methods can find an optimal path from a start structure to a single target structure, or optimize the processing paths to one of the equivalent target-structures in the set.
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement Learning
A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawing process.
Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes
In industrial applications of adaptive optimal control often multiple contrary objectives have to be considered.
