Press-forming of aluminum foam and estimation of its mechanical properties from X-ray CT images using machine learning

Abstract

Forming aluminum foam into the desired shape is essential for actual product application, but aluminum foam is difficult to form. In this investigation, we attempted to press-form aluminum foam immediately after foaming the precursor and employed a neural network to estimate the properties of the press-formed aluminum foam from X-ray CT images. It was found that it was possible to press-form the aluminum foam immediately after foaming while maintaining the pores. The resulting aluminum foam exhibited a similar compressive behavior to non-press-formed aluminum foam. In addition, it was found that a neural network model for the estimation of plateau stress from X-ray CT images of non-press-formed aluminum foam can be created by training on a dataset of X-ray CT images and plateau stress obtained from actual compression tests of aluminum foam. From X-ray CT images, it was also suggested that this neural network model can also be used to estimate the plateau stress of press-formed aluminum foam that retains pores. That is, it was suggested that the neural network model created utilizing X-ray CT images can be employed to estimate the properties of products even when they are press-formed into complex shapes and their properties are difficult to evaluate.