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Deep Learning for Casting Microstructure Inspection: A Convolutional Neural Network Approach

In the present research, we used a convolutional neural network (CNN) to determine secondary dendrite arm spacing (SDAS). The goal was to develop a Deep Learning (DL) model capable of predicting SDAS from optical microscopy images with industrially acceptable prediction accuracy. For the training of the CNN model, polished samples from several widely known casting aluminum alloys were used. In order to build a training set having distinct types of microstructures, alloys cast using different casting processes were used: high-pressure die casting, gravity die casting, and ingot casting. Additional alloys were used in various tasks in order to check model performance. We showed that our CNN structure was able to predict SDAS very well, on all different alloy groups. Additionally, it is also shown that using a higher magnification on the microscope, better accuracy could be obtained because more pixels describe the dendrite structure in that case. Consequently, it is shown that our CNN structure can be used for the task of SDAS prediction within the industry because it achieves an acceptable level of predictive accuracy.

secondary dendrite arm spacing ; convolutional neural network ; casting microstructure inspection ; deep learning ; aluminum alloys

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