Cognition-based self-optimisation of an automotive rear-axle-drive production process

• Autorzy: Schmitt R. , Niggemann C. , Isermann M. , Laass M. , Matuschek N.
• Języki publikacji: EN

Abstrakty

EN

The production of automotive rear-axle drives is a complex process. This is due to many involved process steps, factors and interdependencies between processes, materials, means of production and individuals acting in this environment. In general their effect on product variations is not fully comprehended. Hence, a holistic analytical model is only possible in parts of the production. In this paper a modular approach is presented to make the production more flexible and enable it to react faster on product variations. This is achieved by a Cognitive Production System (CPS), which is based on accumulating, storing and processing of process knowledge so that it can be applied to similar cases. Through the combination and interaction of Cognitive Tolerance Matching (CTM) and Agent-based Systems the performance of the CPS is enhanced. The work discusses the set-up of such a CPS for the production of automotive rear-axle-drives with the focus on the failure state agent.

Słowa kluczowe

EN

Agent-Based Systems; cognition based; self-optimization; rear-axle-drive

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2010
• Tom: Vol. 10, No. 3
• Strony: 68--77
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Schmitt R.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

autor

Niggemann C.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

autor

Isermann M.

• Fraunhofer Institute for Production Technology IPT, Aachen, Germany

autor

Laass M.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

autor

Matuschek N.

• BMW Group, Dingolfing, Germany

Bibliografia

• [1] SCHUH G., KLOCKE F., BRECHER C., SCHMITT R., 2007, Excellence in Production. 1st edition, Apprimus- Verlag, Aachen.
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• [3] JENNINGS N.R., SYCARA K., WOOLDRIDGE M., 1998, A Roadmap of Agent Research and Development. Autonomous Agents and Multi-Agent Systems, 1, 275-306.
• [4] WAGNER T., 2008, Agentenunterstütztes Engineering von Automatisierungsanlagen. Doctoral thesis, Institut für Automatisierungs- und Softwaretechnik, Universität Stuttgart, Stuttgart.
• [5] GÖHNER P., URBANO P.G.A., WAGNER T., 2004, Softwareagenten - Einführung und Überblick über eine Alternative Art der Softwareentwicklung. Teil 3: Agentensysteme in der Automatisierungstechnik. Automatisierungstechnische Praxis. 46/2, 42-51.
• [6] BRECHER C., PYSCHNY N., LOOSEN P., FUNCK M., MORASCH V., SCHMITT R., PAVIM A., 2009, Selfoptimising flexible assembly systems. Workshop on Self-X in mechatronics and other engineering applications, Paderborn, 23-38.
• [7] BEIERLE C., KERN-ISBERNER G., 2006, Methoden wissensbasierter Systeme Grundlagen - Algorithmen - Anwendungen. Vieweg.
• [8] BEETZ M., BUSS M., WOLLHERR D., 2007, Cognitive Technical Systems - What Is the Role of Artificial Intelligence? KI 2007: Advances in Artificial Intelligence, LNAI, 4667, 19-42.
• [9] SCHMITT R., ISERMANN M.; WAGELS C., MATUSCHEK N., 2010, Cognitive optimization of an automotive rear-axle drive production process. Journal of Machine Engineering, 9/4, 71-80