20.500.12556/DKUM-96817 Optimization of reliability and speed of the end-of-line quality inspection of electric motors using machine learning Consistently maintaining high-end product quality in the production process is challenging. End-quality inspection must be highly sensitive to detect even minimal deviations, while being fast and accurate. However, quality inspection systems often face calibration intricacies, are time-consuming, and rely heavily on expert knowledge. They handle substantial data flows and inspect numerous features, some of which contribute minimally to the final grade. To address these challenges, the paper proposes employing statistically supervised machine learning methods for classification. Decision trees, Random forests, Bagging, and Gradient boosting classifiers are recommended for feature selection and accurate diagnosis, particularly for electric motor classification. By utilizing the feature importance attribute for feature selection, the proposed approach compares model accuracies, reducing rampup and commission times significantly. The study found that all suggested classifiers achieved high accuracy in classifying electric motors in end-of-line quality inspection system. Moreover, they effectively reduced the number of features and optimize database operations. Utilizing a reduced feature set streamlined diagnostic algorithms, accelerated learning, and improved model interpretability, enhancing overall efficiency and comprehension. Furthermore, analysing the feature importance attribute could simplify diagnostic hardware and expedite quality inspection by eliminating unnecessary steps. Newly generated models can also verify expert decisions on feature selection and limit adjustments, enhancing efficiency in production processes. quality inspection fault detection machine learning kontrola kakovosti odkrivanje napak strojno učenje elektromotorji true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-29 08:38:15 2026-01-29 08:38:21 2026-03-16 13:53:43 0000-00-00 00:00:00 2024 0 0 pp 182–196 no. 2 Vol. 19 Jun. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-02-20 2024-05-27 2024-08-29 004 214818307 10.14743/apem2024.2.500 1854-6250 APEM19-2_182-196.pdf APEM19-2_182-196.pdf 1 107EE4A87E0C186132DDA346C67D5470 4e6d4bfad19ee28765cda7a7fcc61b80d71e069828fbde0cce970ad2d6a30da4 d7f9b8c9-fce5-11f0-a012-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202749 https://apem-journal.org/Archives/2024/Abstract-APEM19-2_182-196.html 0 dfd17812-fce5-11f0-a012-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202750 Fakulteta za strojništvo Advances in production engineering & management 0 0 0