Automatic Crack Detection in Sand Molds Using Image Processing and Convolutional Neural Networks

Abstract: Sand casting is used to manufacture large metal workpieces. The processing is executed by pouring molten metal into a sand mold. During the process, the mold is subjected to mechanical and thermal stress. It is of economic interest to inspect the molds for defects that can affect casting results, in the worst case leading to discarded products. This thesis investigates and proposes an automated solution for inspecting surface cracks in sand molds. A hybrid solution using image processing and convolutional neural networks has been developed. The first is to find and implement a crack detection method that can perform equally well or better than a human. The second objective is to investigate the amount of training data needed. Twenty-one machine learning models have been trained to evaluate the impact training data size along with transfer learning, fine-tuning, data augmentation, and image processing have on performance. As a result, it was found that the image processing part of the method is not effective in finding cracks in its current form. However, the convolutional neural network still achieves good performance. The method has been trained and tested on sand mold core images captured with a test workbench along with images of concrete walls and pavement acquired from the SDNET2018 data set. Sand mold images achieve 82% accuracy and 79% recall when training on 90 images while testing on 28 images separate from training. A maximal performance of 97.9% accuracy and 99.7% recall is achieved when training on 5400 SDNET2018 images and then testing on 608 images. When training on 100 SDNET2018 images and tested on the same 608 images, a performance of 86.0% and 96.7% recall is achieved. It is concluded that the proposed solution is feasible. Transfer learning and data augmentation are essential techniques to achieve good performance if a small amount of data is available, while fine-tuning may give a slight performance boost. Further work should be performed considering the impact of curved geometry on performance. Investigating alternative structures of the convolutional neural network and testing alternative hyperparameters may improve generalization performance. The image processing performance may be improved if the manufacturing process is more precisely defined, as parameters can be more optimally tuned.