Applying machine learning to detect structural faults in microscopic images of inserts

Abstract: Today the quality control of inserts at Sandvik is done manually by looking at their crosssections through a microscope. The purpose of this project was to automate the quality control of inserts by exploring machine Learning technique to automatically detect structural faults in microscopic images of the insert. To detect these faults an image processing program was first created to extractevery possible fault feature, and then a convolutional neural network (CNN) wasimplemented and applied to the verification of the faults. The error rate (ER) ofextracting the correct faults in the Image Processing was 11% and from thesepossible faults extracted the CNN could then with a 4% ER identify the actualfaults. The dataset was limited in size and had a lack of systematic consistency inhow the images were taken. As a consequence, the model could not be trainedeffectively and therefore, the system did not perform adequate enough for directimplementation. However, the system shows great potential in automating thequality control, considering that the dataset can be improved by standardizing the way for taking the images and the amount of data can be increased with the time.