Automated defect detection for Fluorescent Penetrant Inspection using Random Forest

Abstract 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.