Application of frequency characteristic extraction in increasing the accuracy of X-ray based thickness gauges used for aluminum alloys employing GMDH neural network.

Radiation-based gauges have been widely utilized in the industry as a dependable, non-destructive method of measuring metal layer thickness. It is only possible to trust the conventional radiation thickness meter when the material's composition is known in advance. Thickness measurement errors are to be anticipated in contexts like rolled metal factories, where the real component of the material could diverge greatly from the stated composition. An X-ray-based device was suggested in this study to measure aluminum sheet thickness and identify the type of its alloys. Transmission and backscattered X-ray energy were recorded using two sodium iodide detectors while a 150 kV X-ray tube in the described detection system was operated. Aluminum layers of varying thicknesses (2-45 mm) and alloys (1050, 3105, 5052, and 6061) were simulated to be placed between the X-ray source and the transmission detector. The development of radiation-based systems used the MCNP code as a very powerful framework to imitate the detecting architecture and the spectra acquired by the detectors. The recorded signals were transferred to the frequency domain using the Fourier transform, and the frequency characteristics were extracted from them. Two GMDH neural networks were trained using these characteristics: one to identify the alloy type and another to determine the aluminum layer's thickness. The classifier network had a 92.2% success rate in identifying the alloy type, while the predictive network had a 1.9% error rate in determining the thickness of the aluminum layer. By extracting important characteristics and using powerful neural networks, this study was able to improve the precision with which aluminum layer thickness was measured and correctly identify the alloy type. The suggested method is used to determine the thickness of aluminum and its alloy sheets and may also be applied to other metals.

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN.

Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction.