Advances in Production Engineering & Management
Volume 20 | Number 2 | June 2025 | pp 277–290
https://doi.org/10.14743/apem2025.2.540
A transfer learning approach to machine learning-based end-of-line quality inspection
Mlinarič, J.; Pregelj, B.; Boškoski, P.; Petrovčič, J.; Dolanc, G.
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A B S T R A C T
Transfer learning is a powerful machine learning technique for accelerating the learning process of a new classification task by using knowledge from an existing and related classification task that has already been trained. This technique addresses situations where the necessary training and test data are unavailable or where data acquisition is costly, difficult, or impractical. Additionally, transfer learning significantly reduces training time compared to training from scratch. This study demonstrates the benefits of transfer learning in developing an end-of-line quality inspection system to produce brushless DC electric motors during preproduction. Decision Tree, Random Forest, Bagging, and AdaBoost classifiers were trained on a dataset of 10,000 instances from a mass-produced motor subtype (A). Knowledge in the form of hyperparameters and feature importance was transferred to classifiers for two preproduction subtypes (B and C), each with only 100 instances. The results show up to a 20 % improvement in classification accuracy and significantly lower misclassification costs when using transfer learning. The study highlights the importance of transfer learning as an effective approach for improving industrial classification tasks, especially in preproduction phases where datasets are typically small and imbalanced.
A R T I C L E I N F O
Keywords • Quality inspection; Fault detection; Machine learning; Transfer learning; Decision trees; Random forests; Bagging; AdaBoost
Corresponding author • Mlinarič, J.
Article history • Received 12 February 2025, Revised 23 June 2025, Accepted 27 June 2025
Published on-line • 29 July 2025
E X P O R T C I T A T I O N
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