20.500.12556/DiRROS-27393 AI-enabled end-of-line quality control in electric motor manufacturing methods, challenges, and future directions AI-enabled end-of-line quality control in electric motor manufacturing: methods, challenges, and future directions End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely primarily on manually crafted features, expert-defined thresholds, and rule-based decision logic. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and transfer learning (TL), have emerged as promising solutions to overcome these limitations by enabling data-driven, adaptive, and scalable quality inspection. This paper presents a comprehensive and structured review of the latest advances in intelligent EoL quality inspection for electric motor production. It systematically surveys the non-invasive measurement techniques that are commonly employed in industrial environments and examines the evolution from traditional signal processing-based inspection to AI-based approaches. ML methods for feature selection and classification, DL models for raw signal-based fault detection, and TL strategies for data-efficient model adaptation are critically analyzed in terms of their effectiveness, robustness, interpretability, and industrial applicability. Furthermore, this work identifies key challenges that prevent the widespread adoption of AI-based EoL inspection systems, including dependence on expert knowledge, limited availability of labeled fault data, generalization between motor variants and production condition, and the lack of standardized evaluation methodologies. Based on the identified research gaps, this review outlines research directions and emerging concepts for developing robust, interpretable, and data-efficient intelligent inspection systems suitable for real-world manufacturing environments. By synthesizing recent advances and highlighting open challenges, this review aims to support researchers and experts in designing next-generation intelligent EoL quality control systems that enhance production efficiency, reduce operational costs, and improve product reliability. fault detection condition monitoring zaznavanje napak true false true MDPI Švica Angleški jezik Slovenski jezik © 2026 by the authors. Neznano 2026-02-04 14:15:16 2026-02-04 14:15:16 2026-02-05 03:47:57 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Soavtorji: Boštjan Pregelj, Pavle Boškoski, Janko Petrovčič, Gregor Dolanc; Opis vira z dne 4. 2. 2026; str. 1-23 iss. 2, [article no.] 149 Vol. 14 Feb. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-12-30 2026-01-19 2026-01-28 004.8 2075-1702 10.3390/machines14020149 267289859 https://www.mdpi.com/2075-1702/14/2/149 1 8e50cb68-01cb-11f1-93b0-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=40058 RAZ_Mlinaric_Jernej_2026.pdf RAZ_Mlinaric_Jernej_2026.pdf 1 7DE4EC8C2A1DA3BC43CCA44F86865B96 5ba4f1c6187829248de6b05a07ff9e804d14521acb3486946dccc578160df735 e1615d38-01cb-11f1-93b0-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=40059 Institut Jožef Stefan 0 0 0