Classification of Mechanical Properties of Aluminum Foam by Machine Learning

Abstract

In this study, the mechanical properties of aluminum foam were classified by machine learning from their X-ray computed tomography (CT) images. It was found that aluminum foam samples with high and low compressive strengths can be classified with an accuracy rate of more than 95%. In addition, it was indicated that the accuracy rate can be further improved by increasing the amount of training data. From these results, it is expected that the quality assurance method of aluminum foam can be established by nondestructively acquiring the images of the manufactured aluminum foam product.

Fig. 4 Accuracy rate for each combination in (a) Case 1 and (b) Case 2.