ImageMech: From Image to Particle Spring Network for Mechanical Characterization
Journal
Frontiers in Materials
Journal Volume
8
Date Issued
2022
Author(s)
Abstract
The emerging demand for advanced structural and biological materials calls for novel modeling tools that can rapidly yield high-fidelity estimation on materials properties in design cycles. Lattice spring model, a coarse-grained particle spring network, has gained attention in recent years for predicting the mechanical properties and giving insights into the fracture mechanism with high reproducibility and generalizability. However, to simulate the materials in sufficient detail for guaranteed numerical stability and convergence, most of the time a large number of particles are needed, greatly diminishing the potential for high-throughput computation and therewith data generation for machine learning frameworks. Here, we implement CuLSM, a GPU-accelerated compute unified device architecture C++ code realizing parallelism over the spring list instead of the commonly used spatial decomposition, which requires intermittent updates on the particle neighbor list. Along with the image-to-particle conversion tool Img2Particle, our toolkit offers a fast and flexible platform to characterize the elastic and fracture behaviors of materials, expediting the design process between additive manufacturing and computer-aided design. With the growing demand for new lightweight, adaptable, and multi-functional materials and structures, such tailored and optimized modeling platform has profound impacts, enabling faster exploration in design spaces, better quality control for 3D printing by digital twin techniques, and larger data generation pipelines for image-based generative machine learning models. Copyright ? 2022 Chiang, Chiu and Chang.
Subjects
CUDA (compute unified device architecture)
lattice spring model (LSM)
mechanical characterisation
modeling and simulation
parallel computing
3D printers
C++ (programming language)
Computer aided design
Functional materials
Machine learning
Materials properties
Network architecture
Parallel processing systems
Compute unified device architecture
Data generation
Device architectures
Emerging demands
Lattice spring model
Mechanical characterizations
Model and simulation
Parallel com- puting
Spring networks
Biological materials
Type
journal article