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Production optimization by cognitive technologies

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Today, value chains are considered fractionally and on the basis of simplified model assumptions. Interactions between processes, materials, means of production and individuals acting in this environment as well as the effect of changes on the product usually are not known exhaustively. In order to take corrective actions towards these deficits, self-optimizing production system technologies can be used. They provide systems that emulate the "human" ability of reaching a decision with technical architectures. The goal of these approaches is to steadily analyze and evaluate the actual status in technological as well as in organisational areas and conduct a system adaptation to alternating objectives. Central questioning in this field of research is how to survey production data in order to detect correlations of production parameters and their influence on product parameters, how to derive decisions from this knowledge and how to learn from the consequences. Application technologies capable of taking on these tasks of self-optimization to emulate intelligent behaviour are analysed. The aim is to identify the competencies of these technologies, in order to build a cognitive system architecture based on applications especially suited for each task that has to be fulfilled to emulate cognitive human decision making processes.
Rocznik
Strony
78--90
Opis fizyczny
Bibliogr. 18 poz., tab., rys.
Twórcy
autor
  • Werkzeugmaschinenlabor WZL, RWTH Aachen University, Steinbachstr. 19, 52074 Aachen. Germany
  • Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany
autor
  • Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany
autor
  • Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany
Bibliografia
  • 1. PFEIFER T. ,SCHMITT R., Autonome Produktionszellen. Komplexe Produktionsprozesse flexibel automatisieren, Springer Verlag, Berlin/Heidelberg, 2006.
  • 2. MIELKE A.: Neuronale Netze; 1999.
  • 3. HOFFMANN N., Kleines Handbuch Neuronale Netze: Anwendungsorientiertes Wissen zum Lernen und Nachschlagen; Vieweg, Braunschweig/Wiesbaden, 1993.
  • 4. KRATZER K., Neuronale Netze, Carl Hanser, München/Wien, 1990.
  • 5. LUCZAK H., Arbeitswissenschaften, 2. Aufl., Springer, Berlin, 1998.
  • 6. PUTZER H., Ein uniformer Architekturansatz für kognitive Systeme und seine Umsetzung in ein operatives Framework, Dr. Köster Verlag, Berlin, 2004.
  • 7. SCHMITT R., ISERMANN M., WAGELS C., Cognitive Tolerance Matching, Proceedings of the 6th CIRP International Seminar on Computation in Manufacturing Engineering (ICME), Naples, Italy, 2008.
  • 8. SCHMITT R., DAMM B., PAVIM A., SeIf-Optimised Assembly of a DPSS Laser through Sensor Fusion, 6th CIRP International Seminar, Naples, Italy, 2008.
  • 9. SCHMITT R., HAMMERS C., Governing the Process Chain of Product Development with an enhanced Quality Gate Approach, CIRP Journal of Manufacturing Science and Technology, 2009, Vol. 1, No. 3, 206-211.
  • 10. SCHMITT R, KRISTES D, BEAUJEAN P, Entrepreneurial Quality Management – A new Definition of Quality, IEEE International Engineering Management Conference, New Jersey, 2008, 275-280.
  • 11. SCHMITT R, SCHARRENBERG C, Planning, control and improvement of cross-site production process chains, Proc. 40th CIRP International Manufacturing Systems Seminar, Liverpool, Great Britain, 2007.
  • 12. SCHMITT R., BEAUJEAN P., Selbstoptimierende Produktionssysteme, ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb, 2007, Vol. 102, No. 9, 520-524.
  • 13. Soar Technology: www.soartech.com, 01.12.2008.
  • 14. STROHNER H., Kognitive Systeme. Eine Einführung in die Kognitionswissenschaft, Westdeutscher Verlag, Opladen, 1995.
  • 15. ZAH M. F. et al., The Cognitive Factory, ElMARAGHY, H. A. (Hrsg.): Changeable and Reconfigurable Manufacturing Systems, Springer, Berlin/Heidelberg, 2008.
  • 16. ZAH M. F. et al., Kognitive Produktionssysteme – auf dem Weg zur intelligenten Fabrik der Zukunft, ZWF-Zeitschrift für wirtschaftlichen Fabrikbetrieb, 2007, Vol. 102, No. 9, 525-530.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9c216291-b5e2-47c0-8576-03e68881b4bd
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