20.500.12556/DKUM-96465 A hybrid AI-driven knowledge-based expert system for optimizing gear design a case study for education 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. fracture mechanics principle of universality crack incubation gear assemblies expert system inteligent tuturing system engineering education mehanika loma načelo univerzalnosti inkubacija razpok sklopi zobnikov ekspertni sistem inteligentni sistem za učenje inženirsko izobraževanje true false true Angleški jezik Slovenski jezik Znanstveno delo 2026-01-13 09:25:59 2026-01-13 09:25:59 2026-01-14 03:14:43 0000-00-00 00:00:00 2026 0 0 25 str. št. 1, št. članka 25 Letn. 18 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 2025-12-18 2026-01-01 004.8:37 264450563 10.3390/fi18010025 1999-5903 RAZ_Abersek_Boris_2026.pdf RAZ_Abersek_Boris_2026.pdf 1 DD3A5728A5BAD97815FDD587DD317B38 5a7dc6749e7e044e2646cef72e3adfcb8619cbe961e2f5c5c48fabcb953b56fa c684bf74-f059-11f0-823a-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=201937 https://doi.org/10.3390/fi18010025 1 7e22cd5b-f059-11f0-823a-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=201936 Fakulteta za naravoslovje in matematiko 0 0 0