ImageMech: From image to particle spring network for mechanical characterization

TL;DR

This paper introduces a GPU-accelerated particle spring network modeling toolkit that enables rapid, high-fidelity mechanical characterization of materials from images, facilitating faster design and data generation for machine learning.

Contribution

The authors develop CuLSM, a GPU-accelerated lattice spring model, and Img2Particle, an image-to-particle conversion tool, for efficient material property estimation and fracture analysis.

Findings

CuLSM achieves faster simulation times with high numerical stability.

The toolkit enables high-throughput data generation for machine learning.

It effectively characterizes elastic and fracture behaviors of materials.

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 (LSM), 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 CUDA 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.