Abstract
Modern ultrasonic inspections utilize ever-richer data-sets made possible by phased array equipment. A typical inspection may include tens of channels with different refraction angle, that are acquired at high speed. These rich data sets allow highly reliable and efficient inspection in complex cases, such as dissimilar metal or austenitic stainless steel welds. The rich data sets allow human inspectors to detect cracks with low signal-to-noise ratio from the wider signal patterns. There’s a clear trend in the industry to even richer data sets with full matrix capture (FMC) and related techniques. Convolutional neural networks have recently shown capability to detect flaws with human level accuracy in ultrasonic signals at the B-scan level. To enable automated flaw detection at human-level accuracy for critical applications, these neural networks need be developed to take advantage of today’s rich phased array data-sets. In the present paper, we extend previous work and develop convolutional neural networks that perform highly reliable flaw detection on typical multi-channel phased array data on austenitic welds. The results show, that the modern neural networks can accommodate the rich ultrasonic data and display high flaw detection performance.
Highlights
Conventional ultrasonic weld inspection requires multiple physical probes with different angles to achieve satisfying results
Phased array probes consist of a transducer system of multiple elements that can be controlled, pulsed and received, separately
Ultrasonic data was acquired with the same procedure as used for normal pre-service ultrasonic inspection in nuclear power plants (NPPs) and what is normally recommended for austenitic welds [13]
Summary
Conventional ultrasonic weld inspection requires multiple physical probes with different angles to achieve satisfying results. Phased array probes consist of a transducer system of multiple elements that can be controlled, pulsed and received, separately. By controlling the transducer elements through focal laws one probe can be used to produce different beam angles, beam steering and focus depths. For weld inspections, mechanised scanning allows for the inspection data to be recorded consistently and more importantly allow more thorough data analysis possibilities afterwards. This is possible for conventional probes, it requires multiple individual scans and probe angles making it more time consuming.
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