Human-machine collaborative automation strategies for ultrasonic phased array data analysis of carbon fibre reinforced plastics

NDE 4.0 represents the integration of recent advancements in robotics, sensor technology, and Artificial Intelligence (AI), transforming and automating traditional NDE in line with Industry 4.0 principles. Despite these advancements, data analysis in NDE is still largely performed manually or with traditional rule-based tools such as signal thresholding. These tools often struggle to effectively manage complex data patterns or high noise levels, leading to unreliable defect detection. Additionally, they require frequent manual adjustments to set appropriate parameters for varying inspection conditions, which can be inefficient and error-prone in dynamic or fast paced environments. In contrast, AI-based analysis tools have demonstrated improvements over traditional methods, offering greater accuracy in defect detection and adaptability to higher variability within captured signals. However, their adoption in industrial settings remains limited due to challenges associated with model trust and their “black box” nature. Additionally, practical guidelines for implementing AI tools into NDE workflow are rarely discussed, motivating this work to explore various integration strategies across different automation levels. Three levels of automation were explored, ranging from basic AI-assisted workflows, where tools provide suggestions, to advanced applications where multiple AI models simultaneously process data in a comprehensive analysis, shifting human operators to a supervisory role. Proposed strategies of AI integration into the NDE automation workflow were evaluated on inspection of two defective complex-geometry carbon fibre-reinforced plastics components, commonly used in aerospace and energy sectors for safety-critical structures such as aircraft fuselages and wind turbine blades. The experimental scans were conducted using a phased array ultrasonic testing roller probe mounted on an industrial manipulator, closely replicating industrial practices, and successfully identifying 36 manufactured defects through a combination of supervised object detection on amplitude C-scans, unsupervised anomaly detection on ultrasonic B-scans, and a self-supervised AI model for processing full volumetric ultrasonic data. This inclusion of multiple AI models led to an improvement of up to 17.2 % in the F1 score compared to single-model approaches. Unlike manual inspections, which take hours for larger components, the proposed approach completes the analysis in 94.03 and 57.01 s for the two inspected samples, respectively.