%0 Journal Article %1 BannatBautzeEtAl11tase %A Bannat, Alexander %A Bautze, Thibault %A Beetz, Michael %A Blume, Jürgen %A Diepold, Klaus %A Ertelt, Christoph %A Geiger, Florian %A Gmeiner, Thomas %A Gyger, Tobias %A Knoll, Alois %A Lau, Christian %A Lenz, Claus %A Ostgathe, Martin %A Reinhart, Gunther %A Roesel, Wolfgang %A Ruehr, Thomas %A Schuboe, Anna %A Shea, Kristina %A Stork genannt Wersborg, Ingo %A Stork, Sonja %A Tekouo, William %A Wallhoff, Frank %A Wiesbeck, Mathey %A Zaeh, Michael F. %D 2011 %J IEEE Transactions on Automation Science and Engineering %K 01614 ieee paper embedded ai cognitive processing factory engineering plan assist user robot interaction learn optimize zzz.sfit %N 1 %P 148--174 %R 10.1109/TASE.2010.2053534 %T Artificial Cognition in Production Systems %V 8 %X Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally and situationally dependent triggered paths on state-based graphs are described in this paper.

@article{BannatBautzeEtAl11tase, abstract = {Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally and situationally dependent triggered paths on state-based graphs are described in this paper.}, added-at = {2014-03-20T16:32:08.000+0100}, author = {Bannat, Alexander and Bautze, Thibault and Beetz, Michael and Blume, J\"{u}rgen and Diepold, Klaus and Ertelt, Christoph and Geiger, Florian and Gmeiner, Thomas and Gyger, Tobias and Knoll, Alois and Lau, Christian and Lenz, Claus and Ostgathe, Martin and Reinhart, Gunther and Roesel, Wolfgang and Ruehr, Thomas and Schuboe, Anna and Shea, Kristina and Stork genannt Wersborg, Ingo and Stork, Sonja and Tekouo, William and Wallhoff, Frank and Wiesbeck, Mathey and Zaeh, Michael F.}, biburl = {https://www.bibsonomy.org/bibtex/29d9e085bf07e659296aa06cd056e2b52/flint63}, doi = {10.1109/TASE.2010.2053534}, file = {IEEE Digital Library:2011/BannatBautzeEtAl11tase.pdf:PDF;Related Web Site:http\://www.cotesys.org/:URL}, groups = {public}, interhash = {d0595d7c73b8771c9e8619a73783ff0d}, intrahash = {9d9e085bf07e659296aa06cd056e2b52}, issn = {1545-5955}, journal = {IEEE Transactions on Automation Science and Engineering}, keywords = {01614 ieee paper embedded ai cognitive processing factory engineering plan assist user robot interaction learn optimize zzz.sfit}, month = {#jan#}, number = 1, pages = {148--174}, timestamp = {2018-04-16T11:36:22.000+0200}, title = {Artificial Cognition in Production Systems}, username = {flint63}, volume = 8, year = 2011 }