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Cognition-Enhanced, Self-optimizing Assembly Systems

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Integrative Production Technology

Abstract

Due to shorter product lifecycles and a rising demand for customization, flexibility and adaptability of assembly processes will become key elements in achieving sustainable success of industrial production in high-wage countries. Cognition-enhanced self-optimization as presented in this chapter has been identified as one major contributor to the enhancement of this flexibility and adaptability. The proposed approach to realize cognition-enhanced self-optimization for assembly systems in a broad range of application domains is to integrate dynamic behavior allowing reactions on disturbances and unforeseen events by dynamically adapting the target objectives of internal control loops. Unlike the approach of traditional closed control loops in which target objectives of an optimization process are determined in advance, this approach defines goal functions as dynamically adaptable throughout the process. The chapter concludes with two application examples—one dealing with the assembly of large-scale components (airplane structures) and the other with small component assembly (micro-optical elements)—presented to illustrate the industrial deployment of self-optimization for assembly tasks.

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Notes

  1. 1.

    The MicroSlab laser is a miniaturized laser for marking and engraving applications.

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Authors and Affiliations

  1. Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Steinbachstraße 19, 52074, Aachen, Germany

    Robert Schmitt, Christian Brecher, Walter Kimmelmann, Felix Bertelsmeier, Christoph Knut Storm & Markus Janßen

  2. Department of Mechanism Theory and Dynamics of Machines (IGM), RWTH Aachen University, Kackertstr. 16-18, 52072, Aachen, Germany

    Burkhard Corves, Mathias Hüsing, Tim Detert & Stefan Kurtenbach

  3. Chair for Technology of Optical Systems (TOS), RWTH Aachen University, Steinbachstraße 15, 52074, Aachen, Germany

    Peter Loosen, Jochen Stollenwerk, Martin Holters & Marcel Prochnau

  4. Cybernetic-Cluster IMA/ZLW & IfU, RWTH Aachen University, Dennewartstraße 27, 52068, Aachen, Germany

    Sabina Jeschke

  5. Fraunhofer Institute for Production Technology (IPT), Steinbachstraße 17, 52074, Aachen, Germany

    Sebastian Haag & Eike Permin

  6. Institute of Information Management in Mechanical Engineering (IMA), RWTH Aachen University, Dennewartstraße 27, 52068, Aachen, Germany

    Max Hoffmann

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  1. Robert Schmitt
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Correspondence to Robert Schmitt .

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  1. Lehrstuhl für Werkzeugmaschinen, RWTH Aachen, Aachen, Nordrhein-Westfalen, Germany

    Christian Brecher

  2. RWTH Aachen, Werkzeugmaschinenlabor WZL der RWTH Aachen, Aachen, Germany

    Denis Özdemir

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Schmitt, R. et al. (2017). Cognition-Enhanced, Self-optimizing Assembly Systems. In: Brecher, C., Özdemir, D. (eds) Integrative Production Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-47452-6_10

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