Owing to its lightweight and excellent shock-absorbing properties, aluminum foam is used in automotive parts and construction materials. If a nondestructive quality assurance method can be established, the application of aluminum foam will be further expanded. In this study, we attempted to estimate the plateau stress of aluminum foam via machine learning (deep learning) using X-ray computed tomography (CT) images of aluminum foam. The plateau stresses estimated by machine learning and those actually obtained using the compression test were almost identical. Consequently, it was shown that plateau stress can be estimated by training using the two-dimensional cross-sectional images obtained nondestructively via X-ray CT imaging.

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.