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IDENTIFICATION OF OPTIMIZATION AREAS OF A TRANSTIBIAL PROSTHESIS THROUGH THE POTENTIALS OF ADDITIVE MANUFACTURING PROCESSES

Published online by Cambridge University Press:  27 July 2021

Kay-Eric Steffan ,
Michel Fett ,
Daniel Kurth  and
Eckhard Kirchner
Kay-Eric Steffan*
Affiliation:
Technische Universität Darmstadt
Michel Fett
Affiliation:
Technische Universität Darmstadt
Daniel Kurth
Affiliation:
Technische Universität Darmstadt
Eckhard Kirchner
Affiliation:
Technische Universität Darmstadt
*
Steffan, Kay-Eric Werner Heinz, Technische Universität Darmstadt, Produktentwicklung und Maschinenelemente pmd, Germany, [email protected]

Abstract

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Additive manufacturing enables new possibilities for the design of end products. These are rooted in the potentials of the manufacturing technology, such as flexible, tool-free production. These potentials can be used for the economic and flexible production of customized products. To support the use of the potentials, a development method was created which identifies optimization areas within a product. Therefore, the complexity is reduced by using of product functions. Characteristic functions and structural configurations are used to identify optimization areas. This contribution describes the application of the new development method to an existing mechanical transtibial prosthesis. In doing so optimization areas are identified which may make use of the potentials provided by additive manufacturing. One area is the interface between the prosthesis and the ground. By analyzing walking environments and the gait cycle the need for walking assistance on deformable surfaces was identified. Significant improvements were achieved through a functional integrated, additive manufactured foot sleeve.

Keywords

Design for Additive Manufacturing (DfAM)Product modelling / modelsAdditive Manufacturingtranstibial prosthesisBiomedical design
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1807 - 1816
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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