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

• A comprehensive benchmarking of state-of-the-art deep learning models for defect detection in additive manufacturing using thermal imaging is presented. • The study evaluates classical, efficient, hybrid, and custom Convolutional Neural Network (CNN) architectures under consistent training conditions. • The proposed custom CNN achieves the highest accuracy (97.26%) with efficient memory usage (1.91 GB), suitable for deployment in constrained environments. • Thermal imaging effectively identifies key defects such as under-extrusion, over-extrusion, and warping across all models. • Results provide actionable insights for balancing accuracy, computational efficiency, and deployment feasibility in real-time quality control systems. • Systematic benchmark of seven deep learning architectures for thermal imaging-based defect detection in additive manufacturing across five defect categories: under-extrusion, over-extrusion, warping, layer shifting, and normal print. • Proposed custom CNN achieves 97.26% accuracy while ResNet-18 and MobileNetV2 demonstrate competitive performance (97.19% and 97.07%) without thermal-specific modifications. • Comprehensive GPU memory profiling reveals significant variation across architectures (0.84-6.67 GB), with ResNet-18 and Proposed CNN offering optimal balance for resource-constrained deployment. • Narrow performance gap between top architectures (0.19%) indicates that advanced training protocols contribute more to performance than architectural complexity in thermal defect detection applications. 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 (VGG-16, AlexNet), modern efficient designs (ResNet-18, MobileNetV2, EfficientNet-B0), a hybrid approach (Hybrid VGG-AlexNet), and a proposed custom Convolutional Neural Network (CNN) approach using similar training conditions and a balanced dataset of 21,127 thermal printing images spanning five defect categories: under-extrusion, over-extrusion, layer shifting, warping, and normal prints. Our comparative analysis reveals that multiple architectures achieve competitive performance: top performers reached 97.26% (custom architecture), 97.19% (ResNet-18), 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, layer shifting and warping. These findings offer practitioners actionable insights for balancing accuracy, computational efficiency, and deployment requirements in additive manufacturing quality control systems.