Generating Meaningful Synthetic Ground Truth for Pore Detection in Cast Aluminum Parts

Abstract

Labeling a data set is a tedious task, especially when identifying small pores in an artifact-prone three-dimensional computed tomography (CT) scan of a die cast. Modern deep learning algorithms have the ability to increase the quality and speed of automated inspection. However, they crave vast amounts of labeled data. Thus, we demonstrate a method to simulate a realistic CT-data set which allows ground truth labels to be derived automatically. We place procedurally generated pores inside lifelike material samples, yielding virtual aluminum parts. Using properties of real materials during the simulation, we are able to create scans comprising the typical CT-artifacts which impede the detection of defects, especially noise, beam hardening, and ring artifacts. To validate the realism of this data set, we use the simulated data to train different defect detection algorithms, including convolutional neural networks, and measure their prediction performance on real data showing the aforementioned artifacts. The corresponding ground truth labeling was derived from scans of higher quality of the same parts.