Comparative analysis of deep learning models for defect detection in additive manufacturing using thermal imaging

Sapan Shah, Dhruv Suraj, Sayed Mohsin Reza, Md Abdur Rahman Bin Abdus Salam, Ali Ashraf, Sheikh Fahad Ferdous,
Comparative analysis of deep learning models for defect detection in additive manufacturing using thermal imaging,
Results in Engineering,
Volume 28,
2025,
108359,
ISSN 2590-1230,
https://doi.org/10.1016/j.rineng.2025.108359.
(https://www.sciencedirect.com/science/article/pii/S2590123025044032)
Abstract: Thermal imaging has emerged as a promising approach for real-time defect detection in additive manufacturing, yet the optimal deep learning architecture for this application remains unclear due to limited comparative studies. This study addresses this research gap by conducting a comprehensive benchmarking analysis of seven distinct deep learning architectures for thermal imaging-based defect detection in additive manufacturing. We systematically evaluated classical networks (VGG16, AlexNet), modern efficient designs (ResNet18, MobileNetV2, EfficientNet-B0), a hybrid approach (Hybrid VGG-AlexNet), and a Proposed custom CNN approach using similar training conditions and a balanced dataset of 21,127 thermal printing images spanning five defect categories. Our comparative analysis reveals that multiple architectures achieve competitive performance: top performers reached 97.26 % (custom architecture), 97.19 % (ResNet18), and 97.07 % (MobileNetV2) accuracy, while hybrid approaches achieved 93.72 % accuracy. Also, The Proposed CNN demonstrates a balanced memory profile, with moderate parameter count and efficient memory usage (1.91 GB), making it a promising candidate for deployment in resource-constrained environments. The results demonstrate the system’s effectiveness within controlled experimental conditions and its effectiveness in identifying anomalies such as under-extrusion, over-extrusion, and warping. These findings offer practitioners actionable insights for balancing accuracy, computational efficiency, and deployment requirements in additive manufacturing quality control systems.
Keywords: Additive manufacturing; Thermal imaging; Defect detection; Deep learning, Deep learning benchmarking; Model comparison; Data augmentation; Memory profiling