3D Model Reconstruction of Aluminum Foam Cross-Sectional Sequence Images Based on Milling

Abstract

This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images. Combining precision milling and image acquisition, high-quality cross-sectional images are obtained. Pore structures are segmented by the U-shaped network (U-Net) neural network integrated with the Canny edge detection operator, ensuring accurate pore delineation and edge extraction. The trained U-Net achieves 98.55% accuracy. The 2D data are superimposed and processed into 3D point clouds, enabling reconstruction of the pore structure and aluminum skeleton. Analysis of pore 01 shows the cross-sectional area initially increases, and then decreases with milling depth, with a uniform point distribution of 40 per layer. The reconstructed model exhibits a porosity of 77.5%, with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%, confirming high fidelity. Equivalent sphere diameters decrease with size, averaging 1.95 mm. Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations, and the stresses in the reconstruction model concentrate on thin cell walls, leading to localized deformations. This method accurately restores the aluminum foam’s complex internal structure, improving reconstruction precision and simulation reliability. The approach offers a cost-efficient, high-precision technique for optimizing material performance in engineering applications.

Keywords

aluminum foam / section milling / cross-sectional sequence images / U-Net neural network / 3D model reconstruction / compression simulation