Enhancing Material Thickness Measurement: Ultrasonic Sensor Data Analysis and Thickness Prediction Using Neural Networks

Abstract

Accurate and non-invasive measurement of material thickness plays an important role across several industry sectors such as aerospace, oil and gas, rail and others. This paper aims to use neural networks as a predictive tool to enhance thickness measurement accuracy of immersed steel samples. In this study, a set of training data is provided through conducting experiments on an immersed wedge sample with varying thickness using the A-scan method. This dataset is used for training a single-layer neural network. To evaluate the performance of the trained neural network, a set of test data is provided on different samples with various thicknesses. Through this study, a promising methodology is demonstrated toward accurate and effective thicknesses prediction using neural networks. The outcomes exhibited good agreement when employing a neural network with the same architecture to predict the void locations in another sample of similar material. Furthermore, the results revealed that this method has achieved an error of less than 3% for thickness prediction and less than 7% for void detection.

Keywords

• data cleaning process
• neural networks
• thickness prediction
• ultrasonic sensor