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Article

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Title

Deep Learning in Design of Semi-Automated 3D Printed Chainmail with Pre-Programmed Directional Functions for Hand Exoskeleton

Authors

[ 1 ] Instytut Technologii Materiałów, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication

2022

Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 16

Article type

scientific article

Publication language

english

Keywords
EN
  • computational intelligence
  • deep learning
  • design
  • 3D printed
  • chainmail
  • flexible shape
  • hand exoskeleton
Abstract

EN The aim of this paper is to refine a scientific solution to the problem of automated or semi-automated efficient and practical design of 3D printed chainmails of exoskeletons with pre-programmed properties (variable stiffness/flexibility depending on direction) reflecting individual user needs, including different types and degrees of deficit. We demonstrate this with the example of using chainmail in a hand exoskeleton, where 3D printed chainmail components can be arranged in a single-layer structure with adjustable one- or two-way bending modulus. The novelty of the proposed approach consists in combining the use of real data from research on the exoskeleton of the hand, new methods of their analysis using deep neural networks, with a clear and scalable design of a 3D printed fabric product that can be personalized (mechanical parameters such as stiffness and bend angles in various directions) to the needs and goals of therapy in a particular patient. So far, this approach is unique, having no equivalent in the literature. This paves the way for a wider implementation of adaptive chainmails based on machine learning, more efficient for more complex chainmail designs.

Date of online publication

12.08.2022

Pages (from - to)

8106-1 - 8106-15

DOI

10.3390/app12168106

URL

https://www.mdpi.com/2076-3417/12/16/8106

Comments

Article Number: 8106

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

2,7

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