Probability of Detection applied to X-ray inspection using numerical simulations

In this work, we apply and adapt established probability of detection (POD) methods on the in-line inspection of aluminium cylinder heads using X-ray computed tomography (XCT). We propose to use the XCT simulation tool SimCT to simulate virtual X-ray radiographs from the specimen including artificial defects, which avoids the manufacturing of specimens with calibrated defects of known type (e.g. pores, inclusions, cracks) and characteristics (e.g. size, shape, location). To quantify the POD, these virtual images are analysed using ZEISS automated defect detection (ZADD) to determine defects automatically. ZADD is a deep learning application for anomaly defect detection, classification and segmentation. To create respective POD curves, we apply a hit/miss approach. We demonstrate our method on artificial defects of different sizes, location and material types. Eight representative defects are discussed in detail together with the generated POD curves as well as their characteristics. We finally discuss the advantages of numerical simulations with respect to the probability of detection in order to quantify and improve detection limits.