The paper presents modern approaches to end-of-line (EoL) quality inspection, i.e. the final inspection stage in the manufacturing process of electric motors, based on artificial intelligence. Traditional EoL systems rely on extensive signal processing and expert-defined features and thresholds, which limits their adaptability and increases dependence on specialist knowledge. To address these limitations, several ma- chine learning approaches are discussed, including ensemble-based classification models, transfer learning, and deep neural networks. The application of ensemble models enables automatic feature selection and implicit threshold determination, resulting in a significant reduction of model complexity while maintaining or improving classification accuracy. Transfer learning is shown to be particularly effective in pre-production scenarios, where only limited training data are available, allowing faster commissioning of quality inspection systems and improved fault detection reliability. Furthermore, deep learning methods based on convolutional and recurrent neural networks, trained directly on vibration and acoustic signals represented as Mel-frequency spectrograms, eliminate the need for manual feature engineering and achieve high classification accuracy even in highly imbalanced industrial datasets. The presented results demonstrate that artificial intelligence-based EoL systems can simplify diagnostic procedures, reduce reliance on expert knowledge, improve robustness to product variations, and enhance the overall adaptability and efficiency of industrial quality inspection processes.
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