Embedded AI and Circuit-Level Design for Thermographic Monitoring of Carbon-Based Polymer Composites

Carbon fibre reinforced polymers (CFRPs) are increasingly used in biomedical and safety-critical applications, where embedded and real-time non-destructive testing (NDT) is essential to ensure structural integrity. This paper presents a cost-effective, AI-assisted thermographic inspection system designed from an embedded electronics and circuit-level perspective. The proposed platform integrates a long-wave infrared (LWIR) sensor, dedicated signal conditioning and power management circuits, and a Raspberry Pi-based processing unit within a unified hardware–software co-design approach. Infrared data acquired under surface heating conditions are processed on-board using a convolutional neural network based on a U-Net architecture, enabling automatic localisation and classification of subsurface defects in CFRP samples. Particular attention is devoted to embedded design constraints, including sensor interfacing, acquisition timing, end-to-end latency, and real-time processing scalability. Experimental results confirm the feasibility of real-time surface heat assessment and the robustness of the proposed architecture in detecting delaminations and voids. The presented system contributes to the development of intelligent embedded inspection electronics and provides a reference design for edge AI-enabled NDT systems in industrial and biomedical applications.