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. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties

Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization

A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this paper, we propose and investigate a deep reinforcement learning approach for the optimization of processing paths. The goal is to find optimal processing paths in the material structure space that lead to target-structures, which have been identified beforehand to result in desired material properties. There exists a target set containing one or multiple different structures. 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. In the latter case, the algorithm learns during processing to simultaneously identify the best reachable target structure and the optimal path to it. The proposed methods belong to the family of model-free deep reinforcement learning algorithms. They are guided by structure representations as features of the process state and by a reward signal, which is formulated based on a distance function in the structure space. Model-free reinforcement learning algorithms learn through trial and error while interacting with the process. Thereby, they are not restricted to information from a priori sampled processing data and are able to adapt to the specific process. The optimization itself is model-free and does not require any prior knowledge about the process itself. We instantiate and evaluate the proposed methods by optimizing paths of a generic metal forming process. We show the ability of both methods to find processing paths leading close to target structures and the ability of the extended method to identify target-structures that can be reached effectively and efficiently and to focus on these targets for sample efficient processing path optimization

Crystallographic texture-property data set originating from a simulated multi-step metal forming process

This publication contains a set of 76980 samples of crystallographic textures (as lists of orientations) and corresponding properties (Youngs modulus E and an anisotropy measure R*, similar to the Lankford coefficients, in three room directions). The data originates from a simulated multi-step metal forming process. The simulation was constrained to perform seven successive process steps of 10% strain at the material point in different directions. In each step, the orientation of the tension operation is chosen randomly from a set of 25 uniformly distributed orientations in the orientation space SO(3)

Sets of exemplary microstructure-property data generated via active learning and numerical simulations

This publication contains three exemplary data sets generated via active learning and numerical simulations. The active learning approach used is query-by-committee. For comparison, data is also generated using classical sampling approachs. The first data set originates from a toy example that is based on an appoximated Dirac delta function, for which data was generated randomly and via query-by-committee. The second example is part of a parameter identification problem in materials modeling, for which data was generated via Latin Hypercube design, a knowledge-based approach and query-by-committee. The third example is about generating artificial bcc rolling textures, for which data was generated via Latin Hypercube design, query-by-committee and an extended query-by-committee approach that prevents sampling in regions out of scope

Artificially generated crystallographic textures of steel sheets and their corresponding properties calculated by a Taylor-type crystal plasticity model

This is a collection of sets of artificially generated crystallographic textures of steel sheets, histogram-based representations of them and their corresponding properties. The properties are calculated using a Taylor-type crystal plasticity model