[HTML][HTML] Deep learning in automated ultrasonic NDE–developments, axioms and opportunities
The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator
manually interpreting data with the support of rudimentary automation tools. Recently, many …
manually interpreting data with the support of rudimentary automation tools. Recently, many …
[HTML][HTML] A review of ultrasonic sensing and machine learning methods to monitor industrial processes
Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …
sensor measurements owing to their strong performance. These techniques also offer …
Smart detection of tomato leaf diseases using transfer learning-based convolutional neural networks
A Saeed, AA Abdel-Aziz, A Mossad, MA Abdelhamid… - Agriculture, 2023 - mdpi.com
Plant diseases affect the availability and safety of plants for human and animal consumption
and threaten food safety, thus reducing food availability and access, as well as reducing …
and threaten food safety, thus reducing food availability and access, as well as reducing …
Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions
Ultrasonic flaw classification in weldment is an active area of research and many artificial
intelligence approaches have been applied to automate this process. However, in the …
intelligence approaches have been applied to automate this process. However, in the …
Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder
The industrial application of deep neural networks to automate the ultrasonic weldment flaw
classification system has some limitations. The major problem that affects the classification …
classification system has some limitations. The major problem that affects the classification …
Augmented ultrasonic data for machine learning
I Virkkunen, T Koskinen, O Jessen-Juhler… - Journal of …, 2021 - Springer
Flaw detection in non-destructive testing, especially for complex signals like ultrasonic data,
has thus far relied heavily on the expertise and judgement of trained human inspectors …
has thus far relied heavily on the expertise and judgement of trained human inspectors …
Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review
Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …
for automated analysis of nondestructive examination (NDE) data. One of the applications of …
[HTML][HTML] A review of synthetic and augmented training data for machine learning in ultrasonic non-destructive evaluation
Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent
years, promoting higher-level automation and decision-making in flaw detection and …
years, promoting higher-level automation and decision-making in flaw detection and …
DefectDet: A deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images
Non-destructive testing (NDT) is a set of techniques used for material inspection and
detection of defects. Ultrasonic testing (UT) is one of the NDT techniques, commonly used to …
detection of defects. Ultrasonic testing (UT) is one of the NDT techniques, commonly used to …
Automated flaw detection in multi-channel phased array ultrasonic data using machine learning
O Siljama, T Koskinen, O Jessen-Juhler… - Journal of Nondestructive …, 2021 - Springer
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 …
equipment. A typical inspection may include tens of channels with different refraction angle …