IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v18y2026i1p25-d1831592.html

A Hybrid AI-Driven Knowledge-Based Expert System for Optimizing Gear Design: A Case Study for Education

Author

Listed:
  • Boris Aberšek

    (Faculty of Natural Science and Mathematics, University of Maribor, 2000 Maribor, Slovenia)

  • Samo Kralj

    (Faculty of Natural Science and Mathematics, University of Maribor, 2000 Maribor, Slovenia)

  • Andrej Flogie

    (Faculty of Natural Science and Mathematics, University of Maribor, 2000 Maribor, Slovenia)

Abstract

This paper presents a hybrid knowledge-based expert system (KBES) designed to predict crack incubation and fatigue life in gear design, serving as both a research tool and an educational resource. While crack growth and initiation are well understood, crack incubation remains a challenging area. The presented expert system (KBES) integrates a novel mathematical model for crack incubation based on analogy and defect analysis principles with an optimization algorithm for gear design. The system uses genetic algorithms to optimize gear parameters, demonstrating a 5–10% deviation from experimental values in a specific gear design problem case study. Based on this KBES and a hybrid approach, we developed a learning environment based on an intelligent tutoring system (ITS) which serves older students (MSc and PhD) as a learning environment for the acquisition of knowledge and, above all, for the development of an in-depth understanding of the phenomena that occur both during incubation and initialization and during the further propagation of cracks in the root of the gear tooth, which is the basis for determining the lifespan of gear transmissions.

Suggested Citation

  • Boris Aberšek & Samo Kralj & Andrej Flogie, 2026. "A Hybrid AI-Driven Knowledge-Based Expert System for Optimizing Gear Design: A Case Study for Education," Future Internet, MDPI, vol. 18(1), pages 1-25, January.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:1:p:25-:d:1831592
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/18/1/25/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/18/1/25/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:18:y:2026:i:1:p:25-:d:1831592. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.