Imperial College London
Portrait Picture

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:2019:10.1016/j.ndteint.2018.10.008,
author = {Shipway, N and Barden, T and Huthwaite, P and Lowe, M},
doi = {10.1016/j.ndteint.2018.10.008},
journal = {NDT and E International},
pages = {113--123},
title = {Automated defect detection for Fluorescent Penetrant Inspection using Random Forest},
url = {http://dx.doi.org/10.1016/j.ndteint.2018.10.008},
volume = {101},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Fluorescent Penetrant Inspection (FPI) is the most widely used NDT method in the aerospace industry. Inspection of FPI is currently done visually and difficulties arise distinguishing between penetrant associated with defects and that due to insufficient wash-off or geometrical indications. This, in addition to the nature of the inspection process, means inspection is largely influenced by human factors. The ability to perform automated inspection would provide increased consistency, reliability and productivity.The Random Forest algorithm was used to detect defects in a number of flat titanium plates which had been processed with FPI and photographed to produce digital images. This method has demonstrated the ability to correctly distinguish between defects and other non-relevant indications with accuracy comparable to a human inspector with a very small number of training examples. These results show the potential for the Random Forest algorithm to be used to detect defects in aerospace components, allowing the entire FPI line to become autonomous.
AU  - Shipway,N
AU  - Barden,T
AU  - Huthwaite,P
AU  - Lowe,M
DO  - 10.1016/j.ndteint.2018.10.008
EP  - 123
PY  - 2019///
SN  - 0963-8695
SP  - 113
TI  - Automated defect detection for Fluorescent Penetrant Inspection using Random Forest
T2  - NDT and E International
UR  - http://dx.doi.org/10.1016/j.ndteint.2018.10.008
UR  - http://hdl.handle.net/10044/1/86351
VL  - 101
ER  -
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