|Theme||Additive Manufacturing, Artificial Intelligence|
|Project title||Sensor- and app-based validation of process and product quality for effort-reduced certification of personalized medical devices (SAViour)|
|Project duration||01.02.2021 – 31.01.2023|
Personalized medical devices are subject to strict safety requirements. Thus, the influence of a large number of parameters in the additive manufacturing of individualized medical components must be validated. The resulting complex approval procedures present SMEs with major methodological and economic challenges.
The research project SAViour is concerned with the development of a quality management system for real-time monitoring of process parameters in additive manufacturing. Product quality is monitored using a quality model based on the machine learning process. The data required for this is collected using a sensor concept developed in-house, which is integrated directly into the 3D printing process. The data obtained can additionally be used for holistic process optimization. The process is implemented and researched using Fused Deposition Modeling (FDM).
The goal is to create an app that enables process correction and documents the quality of the manufactured components and the process.
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Publications about the project
Additive Manufacturing Rathje, A.; Knott, A.-L.; Küster, B.; Stonis, M.; Overmeyer, L.: Einführung einer Insitu-Prozessüberwachung in der additiven Materialextrusion. In: ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb, Walter de Gruyter GmbH, 116. Jg. (2021), H. 10, S. 707-710. DOI: 10.1515/zwf-2021-0156.
Additive manufacturing has established itself in medical technology, where complex and patient-specific products are manufactured. Since additive manufacturing processes are sensitive to changes in process parameters and environmental conditions, quality assurance is a key factor for production. This paper presents the approach for in-situ process monitoring in additive material extrusion.
Additive Manufacturing, 3D printing, Fused Deposition Modeling, quality control, machine learning