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Journal of Intelligent Manufacturing

, Volume 26, Issue 6, pp 1063–1076 | Cite as

Learning and reuse of engineering ramp-up strategies for modular assembly systems

  • Daniele ScrimieriEmail author
  • Robert F. Oates
  • Svetan M. Ratchev
Article

Abstract

We present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.

Keywords

Modular assembly systems Ramp-up Decision support Learning Classification 

Notes

Acknowledgments

This research has been funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Daniele Scrimieri
    • 1
    Email author
  • Robert F. Oates
    • 2
  • Svetan M. Ratchev
    • 1
  1. 1.Department of Mechanical, Materials and Manufacturing EngineeringUniversity of NottinghamNottinghamUK
  2. 2.ICOS Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK

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