Inverse design of adaptive flexible structures using physical-enhanced neural network

Mohammadi, M, Kouzani, AZ, Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X and Zolfagharian, A, 2025. Inverse design of adaptive flexible structures using physical-enhanced neural network. Virtual and Physical Prototyping, 20 (1): e2530732. ISSN 1745-2759

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Abstract

Traditional design and analysis of mechanical metamaterials are complex and time-consuming, owing to their nonlinear characteristics. This paper proposes a computationally efficient inverse design framework to predict the nonlinear strain–stress response considering the buckling behaviour under a tensile load. Design and simulation processes of the structures are based on the reduced order model (ROM) of flexible structures, all within a single software environment, MATLAB/Simscape, using the flexible beam blocks. The physical-enhanced neural network (PENN) design is implemented in MATLAB, utilising the results of the ROM model for training and testing. The ROM model takes 4.5 min on average on a 12-core CPU, whereas the trained PENN predicts the stiffness curve in a fraction of a second on a single-core CPU. After training the model, it was utilised to inverse design the metamaterial structure based on a desired stiffness response. Evolutionary optimisation is employed to iteratively feed various structural parameters into the model to find the optimised parameters of a metamaterial structure that can achieve the desired strain–stress response. The proposed metamaterial structure was experimentally validated through three-dimensional (3D) printing using flexible thermoplastic polyurethane (TPU) filament, demonstrating the efficiency and effectiveness of the proposed methodology.

Item Type:	Journal article
Publication Title:	Virtual and Physical Prototyping
Creators:	Mohammadi, M., Kouzani, A.Z., Bodaghi, M. and Zolfagharian, A.
Publisher:	Taylor & Francis
Date:	18 July 2025
Volume:	20
Number:	1
ISSN:	1745-2759
Identifiers:	Number Type 10.1080/17452759.2025.2530732 DOI 2474053 Other
Rights:	© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	29 Jul 2025 09:22
Last Modified:	29 Jul 2025 09:22
URI:	https://irep.ntu.ac.uk/id/eprint/54042

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