Bisma Mutiargo, Amin Garbout, and Andrew A. Malcolm "Defect detection using trainable segmentation", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500L (27 March 2019); https://doi.org/10.1117/12.2521768
Abstract:
Additive manufacturing processes create the opportunity for freedom of design as it allows parts to be manufactured where conventional methods would fail. Printing methods such as selective laser sintering (SLM), electron beam melting (EBM) and others usually produce micro-porosity [1]. These micro defects can have a major impact on the functionality and lifetime of the components. X-ray Computed Tomography (XCT) is an image acquisition technique that allows a complete three-dimensional capture of an object including its internal features and structures. Typically, an XCT system captures many digital 2D radiography images as the sample is being rotated. A computed tomography algorithm then post-processes the 2D images into a reconstructed 3D digital image that represents the scanned part. This technology is an established method of non-destructive evaluation (NDE) to detect the presence of cracks and large porosity in additively manufactured components. However, micro-defects and cracks are known to be difficult to detect. The lack of distinction between X-ray artefacts due to scattering and beam hardening, and the defects make it impossible for simple intensity-based image processing algorithms, such as thresholding, to reliably detect and quantify the presence of a defect especially as the defect size approaches the imaging resolution. Here, an approach to improve micro-porosity and crack detection through the use of random forest classifier was studied. This method was optimized to detect defects that are very close to the voxel size. To achieve this, trainable segmentation with a random forest classifier was used with three pre-defined classes (Pore, Material, and Air). Random forest classifier is a general ensemble learning method that can be utilized for image classification. It creates a set of decision trees from randomly selected subsets of the training set. It then compiles the probability aggregate of each layer of the node within the tree to decide on the outcome. A reference artefact was designed to digitally simulate a CT scan of internal micro-holes, purposefully cut inside the material to simulate the presence of micro-porosity. Using a Computer-Aided-Design (CAD) model as an input to the aRTist simulation software (from Federal Institute for Materials Research and Testing (BAM). Simulated XCT data was obtained to perform supervised training. Subsequently. Verification of the performance of this approach is compared with results from a commercial software.)