Pipe joints mostly form the weakest points in pipeline networks. Infield joints are prone to various flaws. Thus, the infrastructure industry requires an effective inspection technique. Our work focused on evaluating the performance of chord-type transducers for flaw detection in polyethylene (PE) pipe joints. Various artificially introduced flaws were fabricated and tested for statistical estimation of system performance. A-scans data was gathered to develop and assess the viability of a deep learning approach for automated flaw detection. Such an automated “smart” quality control method aligns with requirements of a nondestructive evaluation (NDE) 4.0 platform which can be utilized to achieve reliable and real-time inspection. In this we will introduce results of our current development, starting with approaches to generic data formats, communication protocols, signal processing, artificial intelligence-based (AI) information generation, and decision making. For each of the aspects, results and prototypical implementations will be provided. This includes a pilot development for modern human-machine-interaction using assistive technologies for manual NDE 4.0 inspection. This gives an outlook on further challenges and possible approaches for requirements in the context of secure data exchange, trusted and reliable AI processing, new standardization procedures, and validation of new “smart” NDE 4.0 ultrasonic inspection systems.
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