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A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly

Research output: Journal contributions › Journal articles › Research › peer-review

Authors

The aerodynamic feeding technology developed at the IFA allows feeding rates up to 800 parts per minute while maintaining high reliability and variant flexibility. The machine's setup procedure requires the adaptation of only four machine parameters. Currently, optimal parameter configurations need to be identified manually. This task is greatly time-consuming and requires a high level of expertise. Prospectively, the machine should utilize an algorithm that autonomously identifies optimal parameter configurations for new workpieces to realize fast setup procedures. This paper presents a genetic algorithm for a self-learning feeding system that has been validated in comprehensive simulation studies.

Original languageEnglish
JournalCIRP Annals - Manufacturing Technology
Volume64
Issue number1
Pages (from-to)5-8
Number of pages4
ISSN0007-8506
DOIs
Publication statusPublished - 2015
Externally publishedYes

    Research areas

  • Algorithm, Assembly, Optimization
  • Engineering

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Other publications by the same author(s)

Understanding Resulting Interdependencies Within Production Planning And Control In Hybrid Manufacturing/Remanufacturing Systems

Schulz, J., Hofmann, H., Rokoss, A. & Schmidt, M., 05.2025, Proceedings of the Conference on Production Systems and Logistics: CPSL 2025: 18th – 21th March 2025, University of San Ignacio de Loyola Lima, Peru. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 114 - 126 13 p. (Proceedings of the ... Conference on Production Systems and Logistics; vol. 7).

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Case study on delivery time determination using a machine learning approach in small batch production companies

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Investigation On The Influence Of Remanufacturing On Production Planning And Control – A Systematic Literature Review

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