Multiple scattering filter: Application to plane defect detection in a nickel alloy

Trottier, Camille; Shahjhan, Sharfine; Schumm, Andreas; Aubry, Alexandre; Derode, Arnaud

doi:10.1063/1.4940498

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…At the same time, studies on defects with varying size, crack depth, and other information are scarce, which is also a major limitation of existing defect-detection research [ 120 , 121 , 122 ]. The following is an experimental summary of some research methods [ 108 , 109 , 110 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 ]. The reference [ 123 , 124 , 125 , 126 ] are the experimental results of ultrasonic detection.…”

Section: Summary Analyses Of the Application Status Of Defect-detementioning

confidence: 99%

“…The reference [ 123 , 124 , 125 , 126 ] are the experimental results of ultrasonic detection. The reference [ 109 , 127 , 128 , 129 , 130 , 131 ] are the experimental results of Filter detection. The reference [ 108 , 110 , 132 ] are the experimental results of other quality detection technologies.…”

Section: Summary Analyses Of the Application Status Of Defect-detementioning

confidence: 99%

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Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

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Section: Summary Analyses Of the Application Status Of Defect-detementioning

confidence: 99%

Section: Summary Analyses Of the Application Status Of Defect-detementioning

confidence: 99%

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

View full text Add to dashboard Cite

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Multiple scattering filter: Application to plane defect detection in a nickel alloy

Cited by 1 publication

References 4 publications

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

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