The FH‑Impuls 2016 I project “Digitalisierung in der Medizintechnik‑Fertigung (DigiMed)” was carried out by the Hochschule Furtwangen in partnership with CoHMed – Connected Health in Medical Mountains under the project number 13FH5I04IA. The project was led by Prof. Dr. Kurt Greinwald and was funded through the state‑level FH‑Impuls programme, which supports innovative research at universities of applied sciences. The collaboration involved a multidisciplinary team of researchers, dental specialists, and manufacturing engineers who jointly defined the project’s objectives, requirements, and deliverables over the course of the grant period, which concluded in early 2023.

The first major work package focused on creating a digitally evidence‑based dental workflow. The team developed a comprehensive global model of the dentition that incorporates functional parameters beyond the usual anatomical and radiological data. This expert system, coupled with a simulation engine, transforms an input occlusal state into an optimised target state that reflects the patient’s functional needs. The model allows clinicians to visualise optimal tooth heights, shapes, and positions, and to predict chewing performance for a range of therapeutic scenarios. By varying occlusal parameters, dentists can simulate multiple treatment options and present the projected outcomes to patients, facilitating shared decision‑making. The workflow culminates in a quality‑control step where a chewing‑function test quantifies the percentage improvement in masticatory performance, thereby demonstrating the clinical benefit of the digitally optimised prosthesis. While the report does not provide explicit numerical performance figures, the integration of functional simulation into the digital fabrication chain represents a significant advance over conventional aesthetic‑driven prosthetic design.

The second work package addressed the digitalisation of manufacturing processes for medical devices. The goal was to enable the production of highly customised components from challenging materials such as specialty steels and titanium alloys. The team designed a rule‑based decision support system that guides operators through the selection of machining strategies and process parameters. This “virtual manufacturing expert” draws on a database of literature values, expert knowledge, and real‑time sensor data to recommend optimal settings. Operators interact with the system via a human‑machine interface, allowing them to execute the suggested parameters within the constraints of their machine’s capabilities. Iterative optimisation, performed by experienced manufacturing engineers, refines the database until the system can autonomously propose and monitor complete machining setups. The project also produced a plug‑in that can be integrated into grinding machine control panels, enabling the export of recommended parameters to standard operator programmes. No material or investment costs were incurred; the effort was limited to personnel time for scientific staff and student assistants.

Overall, the DigiMed project delivered two complementary digital solutions: a patient‑specific occlusal optimisation tool that enhances the functional quality of dental prostheses, and a data‑driven manufacturing assistant that improves the precision and efficiency of machining complex medical components. The collaboration between the university, the industry partner, and the funding body facilitated the translation of research findings into prototypes that can be further developed into marketable products, thereby advancing the digital transformation of medical technology manufacturing.