2023
DOI: 10.1016/j.ultras.2022.106854
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Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review

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Cited by 31 publications
(13 citation statements)
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“…Similar needs were identified in other ML research activities related to nuclear energy regulatory questions [43]. Sun et al [43] examined the state of the art in ML for NDE applications in nuclear energy in-service inspection. Key needs identified in this review included data, algorithm selection, and validation.…”
Section: Explainability Of the Ai Algorithmsmentioning
confidence: 84%
“…Similar needs were identified in other ML research activities related to nuclear energy regulatory questions [43]. Sun et al [43] examined the state of the art in ML for NDE applications in nuclear energy in-service inspection. Key needs identified in this review included data, algorithm selection, and validation.…”
Section: Explainability Of the Ai Algorithmsmentioning
confidence: 84%
“…Testing of this research analysis was carried out by referring to simple and multiple linear regression analysis techniques (Norvadewi et al, 2023). This analysis technique requires several requirements, or stages that must be met, including reliability and validity tests, as well as classical assumption tests (Sun et al, 2023). For each test, it is described as follows:…”
Section: Resultsmentioning
confidence: 99%
“…This section briefly discusses the literature review on ML for ultrasonic NDE, including the review methodology used and related findings. Details can be found in the published journal article [5].…”
Section: Literature Review Summarymentioning
confidence: 99%
“…This report documents the results to date of the assessment of ML for ultrasonic NDE. The initial phase included a literature review, the results of which were disseminated through a peer-reviewed journal article [5]. Section 2 provides an introduction to the background information related to ML terminology.…”
Section: Introductionmentioning
confidence: 99%