Novel Terahertz Nondestructive Method for Measuring the Thickness of Thin Oxide Scale Using Different Hybrid Machine Learning Models

Abstract

Effective control of the thickness of the hot-rolled oxide scale on the surface of the steel strip is very vital to ensure the surface quality of steel products. Hence, terahertz nondestructive technology was proposed to measure the thickness of thin oxide scale. The finite difference time domain (FDTD) numerical simulation method was employed to obtain the terahertz time-domain simulation data of oxide scale with various thickness (0–15 μm). Added Gaussian white noise with a Signal Nosie Reduction (SNR) of 10 dB was used when simulating real test signals, using four wavelet denoising methods to reduce noise and to compare their effectiveness. Two machine learning algorithms were adopted to set up models to achieve this goal, including the classical back-propagation (BP) neural network algorithm and the novel extreme learning machine (ELM) algorithm. The principal component analysis (PCA) algorithm and particle swarm optimization (PSO) algorithm were combined to reduce the dimensions of the terahertz time-domain data and improve the robustness of the machine learning model. It could be clearly seen that the novel hybrid PCA-PSO-ELM model possessed excellent prediction performance. Finally, this work proposed a novel, convenient, online, nondestructive, noncontact, safety and high-precision thin oxide scale thickness measuring method that could be employed to improve the surface quality of iron and steel products.