TY  - CHAP
A1  - Grum, Marcus
A1  - Thim, Christof
A1  - Gronau, Norbert
ED  - Andersen, Ann-Louise
ED  - Andersen, Rasmus
ED  - Brunoe, Thomas Ditlev
ED  - Larsen, Maria Stoettrup Schioenning
ED  - Nielsen, Kjeld
ED  - Napoleone, Alessia
ED  - Kjeldgaard, Stefan
T1  - Aiming for knowledge-transfer-optimizing intelligent cyber-physical systems
T2  - Towards sustainable customization : cridging smart products and manufacturing systems
N2  - Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.
KW  - smart automation
KW  - smart production
KW  - human-machine-interaction
Y1  - 2021
SN  - 978-3-030-90699-3
SN  - 978-3-030-90700-6
SN  - 978-3-030-90702-0
U6  - https://doi.org/10.1007/978-3-030-90700-6_16
SP  - 149
EP  - 157
PB  - Springer
CY  - Cham
ER  -