Biomechanical modeling of human-robot accident scenarios: a computational assessment for heavy-payload-capacity robots

Asad, U, Rasheed, S, Lughmani, WA, Kazim, T, Khalid, A ORCID: https://orcid.org/0000-0001-5270-6599 and Pannek, J, 2023. Biomechanical modeling of human-robot accident scenarios: a computational assessment for heavy-payload-capacity robots. Applied Sciences, 13 (3): 1957. ISSN 2076-3417

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Abstract

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing with increased productivity and flexibility. Workspaces are being transformed into fully shared spaces for performing tasks during human-robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The next technological epoch of Industry 5.0 has a heavy focus on human well-being, with humans and robots operating in synergy. However, the reluctance to adopt heavy-payload-capacity robots due to safety concerns is a major hurdle. Therefore, the importance of analyzing the level of injury after impact can never be neglected for the safety of workers and for designing a collaborative environment. In this study, quasi-static and dynamic analyses of accidental scenarios during HRC are performed for medium-and low-payload-capacity robots according to the conditions given in ISO TS 15066 to assess the threshold level of injury and pain, and is subsequently extended for high speeds and heavy payloads for collaborative robots. For this purpose, accidental scenarios are simulated in ANSYS using a 3D finite element model of an adult human index finger and hand, composed of cortical bone and soft tissue. Stresses and strains in the bone and tissue, and contact forces and energy transfer during impact are studied, and contact speed limit values are estimated. It is observed that heavy-payload-capacity robots must be restricted to 80% of the speed limit of low-payload-capacity robots. Biomechanical modeling of accident scenarios offers insights and, therefore, gives confidence in the adoption of heavy-payload robots in factories of the future. The analysis allows for prediction and assessment of different hypothetical accidental scenarios in HRC involving high speeds and heavy-payload-capacity robots.

Item Type:	Journal article
Publication Title:	Applied Sciences
Creators:	Asad, U., Rasheed, S., Lughmani, W.A., Kazim, T., Khalid, A. and Pannek, J.
Publisher:	MDPI
Date:	2 February 2023
Volume:	13
Number:	3
ISSN:	2076-3417
Identifiers:	Number Type 10.3390/app13031957 DOI 1727792 Other
Rights:	© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	10 Feb 2023 11:17
Last Modified:	10 Feb 2023 11:18
URI:	https://irep.ntu.ac.uk/id/eprint/48242

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