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

Xu, Zhou; Ye, Dongdong; Chen, Jianjun; Zhou, Haiting

doi:10.3390/coatings10090805

Cited by 17 publications

(12 citation statements)

References 67 publications

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“…In advanced processing methods, the principal components of thermography (PCT) and pulsed phase thermography were used for defect contrast and visualization of the whole damaged area in UAV composite panels [112]. To use apixel-wise FFT tool in this image sequence, a heat flow adequacy photo and heat stream stage picture can be generated.…”

Section: Significant Developments In Ndt Tests/techniquesmentioning

confidence: 99%

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Kumpati

Skarka

Ontipuli³

2021

Sensors

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Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

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Section: Significant Developments In Ndt Tests/techniquesmentioning

confidence: 99%

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Kumpati

Skarka

Ontipuli³

2021

Sensors

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“…The data must be uniformly planned to ensure the validity of the data. Normalization methods are usually used for data preprocessing [26,27]. The formulation of the BP model can be expressed as follows:…”

Section: Bp Neural Network Model Optimized By Genetic Algorithmmentioning

confidence: 99%

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

Pan

et al. 2021

Coatings

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Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.

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“…Li and Dai used the k-means algorithm to divide the production data into k clusters and uses the BP neural network to predict the final strip rolling temperature to improve the prediction accuracy [13]. Wu et al improved the ELM algorithm and created a two-hidden layer optimized ELM model, and they applied it to the prediction of bending force in the hot strip rolling process [14,15].…”

Section: Data Miningmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Wang

Chen

2021

Journal of Sensors

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With the continuous development of the manufacturing industry, the requirement for strip steel quality is becoming higher and higher in automobile manufacturing, mechanical processing, and electronic and electrical industries. The precise control of strip quality depends on the accurate prediction of strip quality to a certain extent. However, the data collected by a large number of sensors on the complex strip production line and generated by the computer control system presents the characteristics of high dimensionality, high coupling, and nonlinearity, which brings difficulties to the prediction of strip quality. The continuous production of massive data in the production line also forces steel enterprises to seek new data mining methods, mining the relationship between sensor data to predict and control strip quality. To solve these problems, this paper proposes a GBDBN-ELM model, which is more efficient and more accurate than other algorithms. In this model, the RBM in DBN is replaced with GBRBM, so that RBM no longer depends on the binary distribution, can handle continuity values, and retain more data features. In order to solve the problem of too long DBN training time, this article replaces the BP network in DBN with an ELM regression model. The ELM model predicts the strip quality based on the extracted data abstract features, thereby improving the model’s prediction accuracy and shortening the training time. In this paper, the GBDBN-ELM model is compared with the BP neural network, ELM, and DBN, and root mean square error, R square coefficient of determination, and training time are selected as evaluation indexes of the models. The experimental results show that the improved GBDBN-ELM model can not only improve the accuracy of strip steel quality prediction but also shorten the time of model training. The model proposed in this paper has achieved good results in prediction accuracy and performance.

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

Cited by 17 publications

References 67 publications

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

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