A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

• URL: http://arxiv.org/abs/2111.00916v1
• Date: Wed, 27 Oct 2021 08:25:26 GMT
• Title: A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
• Authors: Tarek Iraki, Lukas Morand, Johannes Dornheim, Norbert Link, Dirk Helm
• Abstract summary: In this paper, we introduce a multi-task learning approach for material texture optimization. The approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese networks.
• Score: 1.6311150636417262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• Consistent machine learning for topology optimization with microstructure-dependent neural network material models [0.0]
  We present a framework for multiscale structures with spatially varying microstructural symmetry and differentiably different microstructural descriptors. Our findings highlight the potential of integrating consistency with density-based design optimization.
  arXiv  Detail & Related papers  (2024-08-25T14:17:43Z)
• Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms [5.380545611878407]
  We propose the application of textit Quality-Diversity algorithms to the field of crystal structure prediction. We employ machine-learning surrogate models to compute the interatomic potential and material properties that are used to guide optimisation. We show the value of using neural networks to model crystal properties and enable the identification of novel composition--structure combinations.
  arXiv  Detail & Related papers  (2024-03-06T07:38:31Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning [82.67551367327634]
  We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
  arXiv  Detail & Related papers  (2021-11-10T21:19:02Z)
• Self-supervised optimization of random material microstructures in the small-data regime [0.0]
  This paper presents a flexible, fully probabilistic formulation of such optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages. We employ a probabilistic, data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives. We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase, random media but envision its applicability encompasses a wide variety of microstructure-sensitive design problems.
  arXiv  Detail & Related papers  (2021-08-05T13:25:39Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• An efficient optimization based microstructure reconstruction approach with multiple loss functions [0.0]
  microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s) In this paper, we integrate statistical descriptors as well as feature maps from a pre-trained deep neural network into an overall loss function for an optimization based reconstruction procedure. A numerical example for the microstructure reconstruction of bi-phase random porous material demonstrates the efficiency of the proposed methodology.
  arXiv  Detail & Related papers  (2021-02-04T04:33:17Z)
• Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization [2.462953128215088]
  A major goal of materials design is to find material structures with desired properties. We propose and investigate a deep reinforcement learning approach for the optimization of processing paths.
  arXiv  Detail & Related papers  (2020-09-21T09:20:24Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.