4D printing soft robots guided by machine learning and finite element models

Zolfagharian, A, Durrant, L, Gharaie, S, Rolfe, B, Kaynak, A and Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X, 2021. 4D printing soft robots guided by machine learning and finite element models. Sensors and Actuators A: Physical: 112774. ISSN 0924-4247

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Abstract

This paper presents a method for four-dimensional (4D) printing of soft pneumatic actuator robot (SPA)s, using nonlinear machine learning (ML) and finite element model (FEM). A FEM is developed to accurately simulate experimental actuation to obtain training data for the ML modeling. More than a thousand data training samples from the hyperelastic material FEM model generated to use as training data for the ML model, which was developed to predict the geometrical requirements of the 4D-printed SPA to realize the bending required for specific tasks. The ML model accurately predicted FEM and experimental data and proved to be a viable solution for 4D printing of soft robots and dynamic structures. This work helps to understand how to develop geometrical soft robots’ designs for nonlinear 4D printing problems using ML and FEM.

Item Type:	Journal article
Publication Title:	Sensors and Actuators A: Physical
Creators:	Zolfagharian, A., Durrant, L., Gharaie, S., Rolfe, B., Kaynak, A. and Bodaghi, M.
Publisher:	Elsevier
Date:	20 April 2021
ISSN:	0924-4247
Identifiers:	Number Type 10.1016/j.sna.2021.112774 DOI S0924424721002375 Publisher Item Identifier 1433613 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	29 Apr 2021 15:06
Last Modified:	20 Apr 2022 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/42785

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