Data-Driven Nonlinear Deformation Design of 3D-Printable Shells

TL;DR

This paper introduces a neural network model trained on experimental data to predict and design 3D-printed shells with specific elastoplastic and hyperelastic deformation behaviors, enabling both forward and inverse design processes.

Contribution

It presents a data-driven approach using neural networks to model complex nonlinear deformation behaviors of shells, facilitating custom design and fabrication.

Findings

Neural network accurately predicts force-displacement behavior.

Validated designs exhibit desired deformation properties.

Inverse design generates shells for various applications.

Abstract

Designing and fabricating structures with specific mechanical properties requires understanding the intricate relationship between design parameters and performance. Understanding the design-performance relationship becomes increasingly complicated for nonlinear deformations. Though successful at modeling elastic deformations, simulation-based techniques struggle to model large elastoplastic deformations exhibiting plasticity and densification. We propose a neural network trained on experimental data to learn the design-performance relationship between 3D-printable shells and their compressive force-displacement behavior. Trained on thousands of physical experiments, our network aids in both forward and inverse design to generate shells exhibiting desired elastoplastic and hyperelastic deformations. We validate a subset of generated designs through fabrication and testing. Furthermore, we demonstrate the network's inverse design efficacy in generating custom shells for several applications.