Fluorescent Penetrant Inspection (FPI) is the most widely used NDT method in the aerospace industry. Inspection of FPI is currently done visually and diculties arise distinguishing between penetrant associated with defects and that due to insucient wash-o or geometrical indications. This, in addition to the nature of the inspection process, means inspection is largely inuenced 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 at 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.

The established Machine Learning algorithm Random Forest (RF) has previously been shown to be effective at performing automated defect detection for test pieces which have been processed using fluorescent penetrant inspection (FPI). The work presented here investigates three methods (two previously proposed in other fields, one novel method) of modifying the FPI RF based on the individual performance of decision trees within the RF. Evaluating based on the F 2 Score, which is the harmonic mean of precision and recall which places a larger weighting on recall, it is possible to reduce the RF in size by up to 50%, improving speed and memory requirements, whilst still gain equivalent results to a full RF. Introducing a performance based weighting or retraining decision trees which fall below a certain performance level however, offers no improvement on results for the increased computation time required to implement.

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