Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

12/22/2016
by   Ruijin Cang, et al.
0

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

READ FULL TEXT

page 11

page 12

page 13

page 14

page 16

page 25

page 26

page 27

research
05/08/2018

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions

Stochastic microstructure reconstruction has become an indispensable par...
research
02/04/2021

An efficient optimization based microstructure reconstruction approach with multiple loss functions

Stochastic microstructure reconstruction involves digital generation of ...
research
05/01/2023

Leveraging Language Representation for Material Recommendation, Ranking, and Exploration

Data-driven approaches for material discovery and design have been accel...
research
02/14/2020

Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization

Homogenization is a technique commonly used in multiscale computational ...
research
05/31/2023

Bio-Inspired 4D-Printed Mechanisms with Programmable Morphology

Traditional robotic mechanisms contain a series of rigid links connected...
research
03/31/2016

Towards Zero-Waste Furniture Design

In traditional design, shapes are first conceived, and then fabricated. ...
research
10/04/2019

A Conditional Generative Model for Predicting Material Microstructures from Processing Methods

Microstructures of a material form the bridge linking processing conditi...

Please sign up or login with your details

Forgot password? Click here to reset