Intelligent multiscale simulation based on process-guided composite database

by   Zeliang Liu, et al.

In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries. The molding process induces spatially varying microstructures across various length scales, while the resulting strongly anisotropic and nonlinear material properties are still challenging to be captured by conventional modeling approaches. To prepare the linear elastic training data for our machine learning tasks, Representative Volume Elements (RVE) with different fiber orientations and volume fractions are generated through stochastic reconstruction. More importantly, we utilize the recently proposed Deep Material Network (DMN) to learn the hidden microscale morphologies from data. With essential physics embedded in its building blocks, this data-driven material model can be extrapolated to predict nonlinear material behaviors efficiently and accurately. Through the transfer learning of DMN, we create a unified process-guided material database that covers a full range of geometric descriptors for short fiber reinforced composites. Finally, this unified DMN database is implemented and coupled with macroscale finite element model to enable concurrent multiscale simulations. From our perspective, the proposed framework is also promising in many other emergent multiscale engineering systems, such as additive manufacturing and compressive molding.


page 1

page 6

page 10

page 11

page 12


A Deep Material Network for Multiscale Topology Learning and Accelerated Nonlinear Modeling of Heterogeneous Materials

The discovery of efficient and accurate descriptions for the macroscopic...

Cell division in deep material networks applied to multiscale strain localization modeling

Despite the increasing importance of strain localization modeling (e.g.,...

Model-data-driven constitutive responses: application to a multiscale computational framework

Computational multiscale methods for analyzing and deriving constitutive...

An FE-DMN method for the multiscale analysis of fiber reinforced plastic components

In this work, we propose a fully coupled multiscale strategy for compone...

FE^ANN - An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

Herein, we present a new data-driven multiscale framework called FE^ANN ...

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

This paper extends the deep material network (DMN) proposed by Liu et al...

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

We present a data-driven workflow to biological tissue modeling, which a...