Version 1
: Received: 4 April 2022 / Approved: 6 April 2022 / Online: 6 April 2022 (08:26:09 CEST)
How to cite:
Nageswaran, C. Ultrasonic System to Automatically Measure Size of Cracks using the Snooker Algorithm, a Simulator and Machine Learning. Preprints2022, 2022040034. https://doi.org/10.20944/preprints202204.0034.v1
Nageswaran, C. Ultrasonic System to Automatically Measure Size of Cracks using the Snooker Algorithm, a Simulator and Machine Learning. Preprints 2022, 2022040034. https://doi.org/10.20944/preprints202204.0034.v1
Nageswaran, C. Ultrasonic System to Automatically Measure Size of Cracks using the Snooker Algorithm, a Simulator and Machine Learning. Preprints2022, 2022040034. https://doi.org/10.20944/preprints202204.0034.v1
APA Style
Nageswaran, C. (2022). Ultrasonic System to Automatically Measure Size of Cracks using the Snooker Algorithm, a Simulator and Machine Learning. Preprints. https://doi.org/10.20944/preprints202204.0034.v1
Chicago/Turabian Style
Nageswaran, C. 2022 "Ultrasonic System to Automatically Measure Size of Cracks using the Snooker Algorithm, a Simulator and Machine Learning" Preprints. https://doi.org/10.20944/preprints202204.0034.v1
Abstract
Cracking in a wide array of industrial components and structures pose a significant threat to their integrity. Detecting cracks using ultrasonic inspection techniques is a widespread activity for economic reasons but there are limitations to the techniques due to the morphology of cracks, such as fatigue cracks. In addition to detection there is a need to measure the size of the cracks which are often within the volume of the material. Ultrasonic techniques are well-suited to look inside the volume of the material but achieving sufficient sensitivity to the tip of the cracks in particular is practically difficult. Without an accurate knowledge of where the tip of the crack lies there can be significant uncertainty in sizing measurements. Machine Learning (ML) techniques are being developed to aid in the inspection and monitoring tasks but presenting the ultrasonic data in a suitable way for ML is very important. Following on from recent work presenting the development of the snooker algorithm to create images termed parameter-spaces, this paper presents how these images can be input into neural network based ML systems to automatically size these critical cracks.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
