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

• URL: http://arxiv.org/abs/2408.15097v1
• Date: Tue, 27 Aug 2024 14:30:06 GMT
• Title: Data-Driven Nonlinear Deformation Design of 3D-Printable Shells
• Authors: Samuel Silverman, Kelsey L. Snapp, Keith A. Brown, Emily Whiting,
• Abstract summary: We propose a neural network trained on experimental data to learn the design-performance relationship between 3D-printable shells and 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.
• Score: 1.5088726951324294
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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