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

• Published in: Materials characterization Vol. 221; p. 114781
• Main Authors: Hangai, Yoshihiko, Sakaguchi, Yuki, Okada, Kenji, Tanaka, Yuuki
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2025
• Subjects: Cellular materials; Deep learning; Mechanical properties; Neural network; X-ray computed tomography (CT); Deep learning; Mechanical properties; Cellular materials; Neural network; X-ray computed tomography (CT)
• ISSN: 1044-5803
• DOI: 10.1016/j.matchar.2025.114781

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. •Press-forming of aluminum foam immediately after foaming was possible while maintaining pores.•Press-formed aluminum foam exhibited similar compression behavior to conventional aluminum foam.•Deep learning model for estimating plateau stress from X-ray CT image of aluminum foam can be created.•Plateau stress of press-formed aluminum foam can be estimated from X-ray CT images.•Estimation can be done in less computation time than creating deep learning model.