Soutenance de thèse : Abdel Rahman DAKAK

Automatic Defect Detection in Industrial CT volumes of Castings/Détection Automatique des Défauts dans des Volumes Tomographiques des Pièces de Fonderie

Doctorant : Abdel Rahman DAKAK

Laboratoire INSA : LVA

Ecole doctorale : ED160 : Electronique, Electrotechnique, Automatique

Industrial X-ray computed tomography (CT) has proven its value as a non-destructive method for inspecting light metal castings. The CT volume generated enables the internal and external geometry of the specimen to be measured, casting defects to be localized and their statistical properties to be investigated. On the other hand, CT volumes are very prone to artifacts that can be mistaken for defects by conventional segmentation algorithms. Based on CT data of aluminium alloy castings provided by industrial partners, we have developed an automatic approach to analyze discontinuities inside CT volumes based on a three-step pipeline: (1) 2D segmentation of CT slices with automatic deep segmentation to detect suspicious greyscale discontinuities; (2) classification of these discontinuities into true alarms (defects) or false alarms (artifacts and noise), using a trained convolutional neural network classifier; (3) localization of the validated defects in 3D to investigate their statistical properties such as sphericity, elongation and compactness. Based on this, the validated 3D defects are then classified into porosities or shrinkage cavities using an SVM classifier and a siamese neural network. The choice of each model and the training results are presented and discussed, as well as the efficiency of the approach as an automatic defect detection algorithm for industrial CT volumes.