Imperial College London
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ProfessorMichaelLowe

Faculty of Engineering, Department of Mechanical Engineering

Head of Department of Mechanical Engineering
 
 
 
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Contact

 

+44 (0)20 7594 7000m.lowe Website

 
 
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Assistant

 

Ms Nina Hancock +44 (0)20 7594 7068

 
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Location

 

577DCity and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shipway:2021:10.1016/j.ndteint.2020.102400,
author = {Shipway, NJ and Huthwaite, P and Lowe, MJS and Barden, TJ},
doi = {10.1016/j.ndteint.2020.102400},
journal = {Independent Nondestructive Testing and Evaluation (NDT and E) International},
pages = {102400--102400},
title = {Using ResNets to perform automated defect detection for Fluorescent Penetrant Inspection},
url = {http://dx.doi.org/10.1016/j.ndteint.2020.102400},
volume = {119},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Fluorescent Penetrant Inspection (FPI) is a popular Non-Destructive Testing (NDT) method which is used extensively in the aerospace industry. However, the nature of FPI means results are susceptible to the effects of human factors and this can lead to variable results, making automation desirable. Previous work has investigated the use of established machine learning method Random Forest to perform automated defect detection for FPI. Whilst good results were obtained, there was still a significant number of false positives being identified as defective. This paper presents work done to investigate the potential of using deep learning methods to perform automated defect detection.A dataset was obtained from a set of 99 titanium alloy test pieces with cracks induced using thermal fatigue loading. These test pieces were repeatedly processed and using data augmentation a large dataset was obtained. This data was used to train a ResNet34 and ResNet50 architecture as well as a Random Forest. Two significant results were obtained. Firstly, the ResNet50 is able to create a network capable of detecting 95% of defects with a false call rate of 0.07. This result far exceeded that obtained using the Random Forest method despite both methods only having access to a small dataset. This demonstrated the strong capability of deep learning architectures. The second result was that increasing the amount of data obtained from non defective regions significantly increases performance. This result is encouraging as this data, obtained from non-cracked parts, can be quickly and cheaply obtained by reprocessing test pieces.
AU  - Shipway,NJ
AU  - Huthwaite,P
AU  - Lowe,MJS
AU  - Barden,TJ
DO  - 10.1016/j.ndteint.2020.102400
EP  - 102400
PY  - 2021///
SN  - 0963-8695
SP  - 102400
TI  - Using ResNets to perform automated defect detection for Fluorescent Penetrant Inspection
T2  - Independent Nondestructive Testing and Evaluation (NDT and E) International
UR  - http://dx.doi.org/10.1016/j.ndteint.2020.102400
UR  - http://hdl.handle.net/10044/1/86345
VL  - 119
ER  -
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