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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)

Field-Based Assessment of Key Influence Factors for Throughput Time Prediction in Job Shop Manufacturing

Reinhold, J., Rokoss, A. & Schmidt, M., 2026, Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond - 44th IFIP WG 5.7 International Conference, APMS 2025, Proceedings. Mizuyama, H., Morinaga, E., Kaihara, T., Nonaka, T., von Cieminski, G. & Romero, D. (eds.). Springer Science and Business Media Deutschland, p. 201-215 15 p. (IFIP Advances in Information and Communication Technology; vol. 769 IFIPAICT).

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Implementierung von Produktrückführungsprozessen in bestehende Produktionsabläufe: Herausforderungen und Anforderungen

Sandner, S., Nübel, M., Schulz, J., Metternich, J. & Schmidt, M., 20.06.2025, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 120, 6, p. 397-403 7 p.

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Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods

Wolter, F., Rokoss, A. & Schmidt, M., 2025, Conference on Production Systems and Logistics: University of San Ignacio de Loyola Lima, Peru, 18th – 21th March 2025, Proceedings. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 374-384 11 p. 32. (Proceedings of the ... Conference on Production Systems and Logistics; no. 7).

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

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Analysis of the relevance of models, influencing factors and the point in time of the forecast on the prediction quality in order-related delivery time determination using machine learning

Rokoss, A., Popkes, L. & Schmidt, M., 2024, Proceedings of the CPSL 2024. Herberger, D. & Hübner, M. (eds.). Hannover: publish-Ing., p. 415-431 17 p. (Proceedings of the Conference on Production Systems and Logistics; vol. 2024).

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