Akademik Veri Yönetim Sistemi
IEEE Access, cilt.13, ss.192173-192188, 2025 (SCI-Expanded, Scopus)
In complex industrial manufacturing processes, various defects may occur in products because of the poor quality of the equipment, materials, and human factors. These defects may lead to undesirable situations due to reasons such as increasing costs, shortening service life, reducing customer satisfaction, and adversely affecting quality. To reduce these problems, it is crucial to detect products with such defects quickly and effectively. Therefore, within the scope of this study, a hybrid Convolutional Neural Network (CNN)-Support Vector Machine (SVM) hybrid model is introduced to improve the classification accuracy of production defects. The proposed framework incorporates a CNN for feature extraction, coupled with a Least Absolute Shrinkage and Selection Operator (LASSO) to facilitate feature selection. This approach aims to mitigate the dimensionality of the feature space and is complemented by the implementation of a SVM classifier. The performance of the model was tested on three widely used publicly available datasets: metal casting, textile fabric, and food images. Significant and high-performance results in both classification accuracy and execution time, which are important performance metrics for real-time applications, were obtained by the proposed hybrid CNN-SVM algorithm. Statistical analyses are also performed to test whether there is a significant difference between the CNN-SVM hybrid and conventional CNN and between CNN-SVM hybrid and the previous studies in literature. This study demonstrates that the AlexNet-SVM hybrid with LASSO outperforms the conventional CNN in terms of execution time. Furthermore, the AlexNet-SVM hybrid without LASSO achieved classification accuracies of 98,29%, 99,82%, and 97,37% for the casting, food, and fabric datasets, respectively.