Enhancing Defect Detection on Surfaces Using Transfer Learning and Acoustic Non-Destructive Testing

Debonding, especially in plastic materials, refers to the separation occurring at the interface within a bonded structure composed of two or more polymeric layers. Due to the great heterogeneity of materials and layering configurations, highly specialized expertise is often required to detect the presence and extent of such defects. This study presents a novel approach that leverages transfer learning techniques to improve the detection of debonding defects across different surface types using PICUS, an acoustic diagnostic device developed at Roma Tre University for the assessment of defects in heritage wall paintings. Our method leverages a pre-trained deep learning model, adapting it to new material conditions. We designed a planar test object embedded with controlled subsurface cavities to simulate the presence of defects of adhesion and air among the layers. This was rigorously evaluated using non-destructive testing using PICUS, augmented by artificial intelligence (AI). A convolutional neural network (CNN), initially trained on this mock-up, was then fine-tuned via transfer learning on a second test object with distinct geometry and material characteristics. This strategic adaptation to varying physical and acoustic properties led to a significant improvement in classification precision of defect class, from 88% to 95%, demonstrating the effectiveness of transfer learning for robust cross-domain defect detection in challenging diagnostic applications.