Combination of a Nondestructive Testing Method with Artificial Neural Network for Determining Thickness of Aluminum Sheets Regardless of Alloy’s Type

Abstract

Non-destructive and reliable radiation-based gauges have been routinely used in industry to determine the thickness of metal layers. When the material’s composition is understood in advance, only then can the standard radiation thickness meter be relied upon. Errors in thickness measurements are to be expected in settings where the actual composition of the material may deviate significantly from the nominal composition, such as rolled metal manufacturers. In this research, an X-ray-based system is proposed to determine the thickness of an aluminum sheet regardless of its alloy type. In the presented detection system, an X-ray tube with a voltage of 150 kV and two sodium iodide detectors, a transmission detector and a backscattering detector, were used. Between the X-ray tube and the transmission detector, an aluminum plate with different thicknesses, ranging from 2 to 45 mm, and with four alloys named 1050, 3050, 5052, and 6061 were simulated. The MCNP code was used as a very powerful platform in the implementation of radiation-based systems in this research to simulate the detection structure and the spectra recorded using the detectors. From the spectra recorded using two detectors, three features of the total count of both detectors and the maximum value of the transmission detector were extracted. These characteristics were applied to the inputs of an RBF neural network to obtain the relationship between the inputs and the thickness of the aluminum plate. The trained neural network was able to determine the thickness of the aluminum with an MRE of 2.11%. Although the presented methodology is used to determine the thickness of the aluminum plate independent of the type of alloy, it can be used to determine the thickness of other metals as well.