Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives

Jin, L, Zhai, X, Xue, W, Zhang, K, Jiang, J, Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X and Liao, W-H, 2025. Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives. Smart Materials and Structures, 34 (3): 033002. ISSN 0964-1726

Preview

Text 2367043_Bodaghi.pdf - Published version Download (5MB) | Preview

Abstract

The current boom in soft robotics development has spurred extensive research into these flexible, deformable, and adaptive robotic systems. However, the unique characteristics of soft materials, such as non-linearity and hysteresis, present challenges in modeling, calibration, and control, laying the foundation for a compelling exploration based on finite element analysis (FEA), machine learning (ML), and digital twins (DT). Therefore, in this review paper, we present a comprehensive exploration of the evolving field of soft robots, tracing their historical origins and current status. We explore the transformative potential of FEA and ML in the field of soft robotics, covering material selection, structural design, sensing, control, and actuation. In addition, we introduce the concept of DT for soft robots and discuss its technical approaches and integration in remote operation, training, predictive maintenance, and health monitoring. We address the challenges facing the field, map out future directions, and finally conclude the important role that FEA, ML, and DT play in shaping the future of soft robots.

Item Type:	Journal article
Publication Title:	Smart Materials and Structures
Creators:	Jin, L., Zhai, X., Xue, W., Zhang, K., Jiang, J., Bodaghi, M. and Liao, W.-H.
Publisher:	IOP Publishing
Date:	March 2025
Volume:	34
Number:	3
ISSN:	0964-1726
Identifiers:	Number Type 10.1088/1361-665X/adadcd DOI 2367043 Other
Rights:	© 2025 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	14 Feb 2025 14:46
Last Modified:	14 Feb 2025 14:46
URI:	https://irep.ntu.ac.uk/id/eprint/53045

Actions (login required)

Edit View

Statistics

Views

Downloads