Hybrid machine learning approaches to piston defect detection in industrial applications

Authors : Oya Kilci, Murat Koklu

Pages : 255-267

Doi:10.18245/ijaet.1776559

View : 67 | Download : 179

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :This study proposes a hybrid artificial intelligence approach for detecting surface defects in industrial piston components by combining deep learning based feature extraction with traditional machine learning classifiers. The experimental analysis was performed using the piston dataset, which includes both defected and perfect samples of industrial pistons. Four classification algorithms, namely Support Vector Machine, Artificial Neural Network, k Nearest Neighbors, and Random Forest, were implemented and compared based on their classification accuracy. The Support Vector Machine achieved the highest performance with an accuracy of 99.84%, demonstrating superior capability in distinguishing between defected and non-defected piston surfaces. The Artificial Neural Network followed closely with an accuracy of 99.69%, showing highly stable and consistent behavior. The k Nearest Neighbors model reached an accuracy of 98.75%, while the Random Forest achieved an accuracy of 94.84%, indicating a comparatively lower generalization performance. The results confirm that the hybrid combination of deep feature extraction and conventional classification methods significantly improves accuracy and robustness in defect detection. The proposed framework contributes to the industry 4.0 vision by providing a reliable, efficient, and intelligent quality control solution suitable for real-time manufacturing systems, supporting digital transformation in modern industrial environments.
Keywords : inceptionV3, Gradient Boosting, Destek Vektör Makineleri, k-En Yakın Komşu, Rastgele Orman, Piston

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