Accurate X-ray-based thickness determination of aluminum sheets using ACO-optimized MLP neural networks

Accurate thickness measurement of aluminum sheets is critical for industries such as aerospace and automotive but is challenged by traditional methods' dependency on known alloy compositions. This study proposes a novel x-ray-based system to determine thickness across four aluminum alloys (1050, 3105, 5052, and 6061) with thicknesses ranging from 1 to 45 mm, independent of composition. Using Monte Carlo N-particle simulations, an optimized multi-layer perceptron (MLP) neural network, and ant colony optimization (ACO) for feature selection, the approach achieves precise predictions with reduced computational complexity. The model demonstrated high accuracy, with a mean relative error (MRE) of 1.06% on test data, outperforming conventional methods. This scalable, calibration-free system offers a robust solution for real-time thickness measurement in diverse industrial applications.