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Additively manufactured textiles and parametric modelling by generative algorithms in orthopaedic applications

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journal contribution
posted on 09.12.2019, 15:02 by Vito Ricotta, Ian Campbell, Tommaso Ingrassia, Vincenzo Nigrelli
Purpose - The purpose of this work is to implement a new process aimed at the design and production of orthopaedic devices fully manufacturable by Additive Manufacturing. In this context, the use of generative algorithms for parametric modelling of Additively Manufactured Textiles has been also investigated and new modelling solutions have been proposed. Design/methodology/approach - A new method for the design of customised elbow orthoses has been implemented. In particular, to better customise the elbow orthosis, a generative algorithm for parametric modelling and creation of a flexible structure, typical of an Additively Manufactured Textile, has been developed. Findings – To test the developed modelling algorithm, a case study based on the design and production of an elbow orthosis made by Selective Laser Sintering was investigated. Obtained results have demonstrated that the implemented algorithm overcomes many drawbacks typical of the traditional CAD modelling approaches. The parametric CAD model of the orthosis obtained through the new approach is characterized by a flexible structure with no deformations or mismatches and has been effectively used to produce the prototype through AM technologies. Originality/value - Obtained results present innovative elements of originality in the CAD modelling sector, which can contribute to solving problems related to modelling for Additive Manufacturing in different application fields.

History

School

  • Design

Published in

Rapid Prototyping Journal

Volume

26

Issue

5

Pages

827 - 834

Publisher

Emerald

Version

AM (Accepted Manuscript)

Rights holder

© Emerald Publishing Limited

Publisher statement

This paper was accepted for publication in the journal Rapid Prototyping Journal and the definitive published version is available at https://doi.org/10.1108/RPJ-05-2019-0140.

Acceptance date

15/11/2019

Publication date

2020-02-12

Copyright date

2020

ISSN

1355-2546

Language

en

Depositor

Prof Ian Campbell . Deposit date: 5 December 2019