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

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 impr...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dongdong Ye, Zhou Xu, Jiabao Pan, Changdong Yin, Doudou Hu, Yiwen Wu, Rui Li, Zhen Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	grit blasting surface roughness corrosion resistance performance GA–BP Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b7f32fd27ffc4df2b49958686657ff5a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b7f32fd27ffc4df2b49958686657ff5a
record_format	dspace
spelling	oai:doaj.org-article:b7f32fd27ffc4df2b49958686657ff5a2021-11-25T17:15:27ZPrediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network10.3390/coatings111112742079-6412https://doaj.org/article/b7f32fd27ffc4df2b49958686657ff5a2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-6412/11/11/1274https://doaj.org/toc/2079-6412Grit 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.Dongdong YeZhou XuJiabao PanChangdong YinDoudou HuYiwen WuRui LiZhen LiMDPI AGarticlegrit blastingsurface roughnesscorrosion resistance performanceGA–BPEngineering (General). Civil engineering (General)TA1-2040ENCoatings, Vol 11, Iss 1274, p 1274 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	grit blasting surface roughness corrosion resistance performance GA–BP Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	grit blasting surface roughness corrosion resistance performance GA–BP Engineering (General). Civil engineering (General) TA1-2040 Dongdong Ye Zhou Xu Jiabao Pan Changdong Yin Doudou Hu Yiwen Wu Rui Li Zhen Li Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
description	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.
format	article
author	Dongdong Ye Zhou Xu Jiabao Pan Changdong Yin Doudou Hu Yiwen Wu Rui Li Zhen Li
author_facet	Dongdong Ye Zhou Xu Jiabao Pan Changdong Yin Doudou Hu Yiwen Wu Rui Li Zhen Li
author_sort	Dongdong Ye
title	Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
title_short	Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
title_full	Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
title_fullStr	Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
title_full_unstemmed	Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network
title_sort	prediction and analysis of the grit blasting process on the corrosion resistance of thermal spray coatings using a hybrid artificial neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b7f32fd27ffc4df2b49958686657ff5a
work_keys_str_mv	AT dongdongye predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT zhouxu predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT jiabaopan predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT changdongyin predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT doudouhu predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT yiwenwu predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT ruili predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork AT zhenli predictionandanalysisofthegritblastingprocessonthecorrosionresistanceofthermalspraycoatingsusingahybridartificialneuralnetwork
_version_	1718412614367707136

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

Ejemplares similares