Process Optimization for the Manufacturing of Individualized Ankle Foot Orthoses via Digitalization and AM
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
To face challenges of the traditional process of individual orthoses, we present a novel approach to automate the needed tasks by rethinking the process chain, bringing data-centricity into an optimized and digitize workflow by using the example of an ankle foot orthosis. The basic idea of a new process chain relies on a continuous and precise modeling process for any kind of prosthetic or orthotic product. Therefore, we define process steps by using fully parametric designs, which are at least reproducible, digitizable or digitized and additive manufacturing producible. These steps include patient data acquirement, parameter transformation, adjusting CAD models for individual designs, verification of the final product model and printing with additive manufacturing (AM). Except AM, everything can be supported by created toolboxes to simplify the process for medical users. So, as a preliminary work for these steps and for a whole process chain, it is essential to go through patient data acquirement and evaluation from CT, MRT, etc. and metadata. Further it is important to design a parametric CAD model built on the underlying biometric of the patient data and to create a related combination of patient and construction parameters. Because simulation data e.g. FEM are usually not available at the orthoses individualization phase, deep learning imprints collected simulation knowledge into the design model adaption by predicting shape feature parameters. The general process chain is then transferred to a specific orthoses use case, where a useful process sequence needs to be adapted in a customized fashion .
Details
Originalsprache | Englisch |
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Titel | Progress in Digital and Physical Manufacturing |
Seiten | 330-338 |
Seitenumfang | 9 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Springer Tracts in Additive Manufacturing (STAM) |
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ISSN | 2730-9576 |
Externe IDs
Scopus | 85201309740 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- individual-AFO, knowledge-based-parametrization, medical-device