CASTING DEFECTS DETECTION IN ALUMINUM ALLOYS USING DEEP LEARNING: A CLASSIFICATION APPROACH

The present research deals with the detection of porosity defects in aluminum alloys using convolutional neural networks (CNNs). The goal of this research is to build a CNN model that can accurately predict porosity defects in light optical microscopy images. To train the model, images of polished samples of several aluminum alloys containing a significant number of defects were used: EN AC 46000 AlSi9Cu3(Fe), EN AC 43400 AlSi10Mg(Fe), EN AC 47100 AlSi12Cu1(Fe), EN AC 51400 AlMg5(Si), EN AC 42000 AlSi7Mg0.6, EN AC-42000 AlSi7Mg and EN AC-44300 AlSi12(Fe)(a). Various types of porosity defects were included. The proposed custom CNN structure performed excellently in the test set: it correctly classified 3,990 images and made errors in only 254 images. Thus, the classification accuracy achieved was 94%. In addition, the performance of the model was tested with all the alloys used during the training at the nominal magnification (50x) as well as with the EN AC 46000 AlSi9Cu3(Fe) alloys at different magnifications (50x, 100x, 200x, 400x, and 500x). Consequently, it is shown that deep learning models can be used to accurately predict porosity defects.

Automated summary

This research focuses on using convolutional neural networks (CNNs) to detect porosity defects in aluminum alloys. Through training and testing with various aluminum alloy samples, the proposed custom CNN model demonstrates accurate prediction of porosity defects with a classification accuracy of 94%.