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Bio-inspired control of automated stem cell production

: Egri, P.; Csáji, B.C.; Kis, K.B.; Monostori, L.; Váncza, J.; Ochs, J.; Jung, S.; König, N.; Schmitt, R.; Brecher, C.; Pieske, S.; Wein, S.

Volltext ()

Procedia CIRP 88 (2020), S.600-605
ISSN: 2212-8271
Conference on Intelligent Computation in Manufacturing Engineering (ICME) <13, 2019, Gulf of Naples>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPT ()

The possible role of stem cells in medical treatments can hardly be overestimated. Today they are produced â almost without exemption â with significant human involvement using adaptive protocols that take the growth behavior of the biological material into account. Automated production platforms are being developed and tested in a number of research laboratories with the main goals of improving reproducibility, as well as increasing quality and throughput. However, automated stem cell production differs from the traditional manufacturing processes in (1) the inherent diversity of the products (stem cells), (2) their varying growth rates and process times, (3) the need for their regular observation and process adaptation, and, therefore, (4) for mixed-initiative production control. A distinctive feature of the domain is the symbiotic coexistence and co-evolution of the technical, ICT and biological ingredients in production structures. A challenging way to overcome these issues is the use of biologically-inspired control algorithms. In the paper the application of reinforcement learning is proposed for this purpose. As a first step, a digital simulation of the stem cell production was performed in order to generate patterns for the training process and to test the approach. In addition to the description of the concept, the paper also presents initial research results.