RPJ_with_Vito.PDF (1.87 MB)
Additively manufactured textiles and parametric modelling by generative algorithms in orthopaedic applications
journal contribution
posted on 2019-12-09, 15:02 authored by Vito Ricotta, Ian Campbell, Tommaso Ingrassia, Vincenzo NigrelliPurpose - 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 JournalVolume
26Issue
5Pages
827 - 834Publisher
EmeraldVersion
- AM (Accepted Manuscript)
Rights holder
© Emerald Publishing LimitedPublisher 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
2019-11-15Publication date
2020-02-12Copyright date
2020ISSN
1355-2546Publisher version
Language
- en