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

Zhou Xu,Jianjun Chen,Haiting Zhou,Dongdong Ye

doi:10.3390/coatings10090805

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.

Highlights

Steel is widely used in modern society and will still be the cornerstone of future industrial development and progress, owing to its rich reserves, low price, excellent mechanical properties, simple smelting, tractable alloying and heat treatment
A portion of the incident THz waves was reflected on the surface of oxide scale, while a portion of it transmitted through the oxide scale and was absolutely reflected at the interface between the oxide scale and substrate, a portion of the reflected terahertz waves from the interface transmitted through the oxide scale surface into air, and a portion of terahertz waves was reflected back into the oxide scale
The BP model was trained 50 times using random disordered samples, and the trained BP model with the best prediction performance among the 50 times training was chosen to measure the oxide scale thickness, the results of each trained BP model were very different, even though the predicted results corresponding to the trained BP model with the best training performance were still unsatisfactory

Summary

Introduction

Steel is widely used in modern society and will still be the cornerstone of future industrial development and progress, owing to its rich reserves, low price, excellent mechanical properties, simple smelting, tractable alloying and heat treatment. With the development of global economy, as the dominating steel and iron product, the proportion of flat rolled products will continue to increase [1,2]. During the process of hot rolling and cooling of the strip, the primary, secondary and tertiary oxide coatings are formed on the surface of the strip, this multilayer oxide coating is generally called oxide scale. The structure and composition of the oxide scale are very complex, and vary greatly with factors such as cooling system and curling temperature. There are six types of iron oxides: hematite. III (α-Fe2 O3 ), magnetite (Fe3 O4 , FeII Fe2 O4 ), maghemite (γ-Fe2 O3 ), wüstite (FeO), β-Fe2 O3 and ε-Fe2 O3. Hot-rolled oxide scale is a hybrid of iron oxides (wüstite, magnetite and hematite), Coatings 2020, 10, 805; doi:10.3390/coatings10090805 www.mdpi.com/journal/coatings

Methods

Results

Conclusion