Additive manufacturing and advanced production management aligns with Industry 5.0 standards, requiring auto- mated and cost-effective Non-Destructive Testing (NDT) solutions for in-line quality assurance. This paper proposes a near-field Synthetic Aperture Radar (SAR) framework for Non-Destructive Testing (NDT) applications using a Commercial Off-The-Shelf (COTS) radar solution together with automated shape classi- fication algorithms for intelligent defect detection. This work integrates a cooperative robotic arm to accomplish a Synthetic Aperture from multiple backscattered perspectives. The acquired data is processed through a SAR algorithm followed by a blob- based post-processing module that automatically detects and classifies geometric shapes and structural features. A preliminary validation of the algorithms was carried out using samples con- taining various geometric shapes and symbols. Results confirm the feasibility of compact SAR imaging using low-cost COTS mmWave radars for practical NDT applications in Industry 5.0 manufacturing environments and establish a foundation for future multimodal inspection systems combining radar and depth sensing.