Automated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine Learning
نویسندگان
چکیده
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 in complex cases, such as dissimilar metal or austenitic stainless steel welds. The human inspectors to detect cracks low signal-to-noise ratio from the wider signal patterns. There’s a clear trend industry even richer full matrix capture (FMC) related techniques. Convolutional neural networks have recently shown capability flaws level accuracy signals B-scan level. To enable automated flaw detection human-level for critical applications, these need be developed take advantage today’s data-sets. In present paper, we extend previous work develop convolutional perform on multi-channel results show, modern can accommodate display performance.
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ژورنال
عنوان ژورنال: Journal of Nondestructive Evaluation
سال: 2021
ISSN: ['1573-4862', '0195-9298']
DOI: https://doi.org/10.1007/s10921-021-00796-4