Detection of elevated regions in surface images from laser beam melting processes

Laser Beam Melting (LBM) is a promising Additive Manufacturing technology that allows the layer-based production of complex metallic components suitable for industrial applications. Widespread application of LBM is hindered by a lack of quality management and process control. Elevated regions in produced layers pose a major risk to process stability as collisions between the powder coating mechanism and the part may occur, which cause damages to either one or even both. We train a classifier-based detector for elevated regions in laser exposure result images. For this purpose we acquire two high resolution layer images: one after laser exposure and another one after powder deposition for the next layer. Ground truth labels for critical regions are obtained from analysis of the latter, where elevated regions are not covered by powder. We compute dense descriptors (HOG, DAISY, LBP) on the surface image after laser exposure and compare their predictive power. The top five descriptor configurations are used to optimize parameters of Random Forest, Support Vector Machine and Stochastic Gradient Descent (SGD) classifiers. We validate the detectors with optimized parameters using cross-validation on 281 images from three build jobs. Using a DAISY descriptor with a SGD classifier we achieve a F1-score of 0.670. The presented method enables detection of elevated regions before powder coating is performed and can be extended to other surface inspection tasks in LBM layer images. Detection results can be used to assess LBM process parameters with respect to process stability during process design and for quality management in production.