Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings

The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposit...

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Autores principales:	Pei Feng, Yuhua Shi, Peng Shang, Hanjun Wei, Tongtong Peng, Lisha Pang, Rongrong Feng, Wenyuan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Ni–W graded coatings backward propagation (BP) neural network pulse electrodeposition high temperature oxidation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/4e1033900cfd4b0280bd98ae2acd9931
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spelling	oai:doaj.org-article:4e1033900cfd4b0280bd98ae2acd99312021-11-25T18:13:31ZApplication of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings10.3390/ma142267811996-1944https://doaj.org/article/4e1033900cfd4b0280bd98ae2acd99312021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/22/6781https://doaj.org/toc/1996-1944The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposition method in order to solve the mechanical mismatch problem between substrate and coatings. More importantly, a backward propagation (BP) neural network was applied to efficiently optimize the pulse electrodeposition process of Ni–W graded coating. The SEM, EDS, XRD, Vickers hardness tester and Weighing scales are used to analyze the micromorphology, chemical element, phase composition, and micro hardness as well as oxidation weight increase, respectively. The results show that the optimal process conditions with BP neural network are as follows: the bath temperature is 30 °C, current density is 15 mA/cm<sup>2</sup> and duty cycle is 0.3. The predicted value of the model agrees well with the experimental value curve, the relative error is minor. The maximum error is less than 3%, and the correlation coefficient is 0.9996. The Ni–W graded coating prepared by BP neural network shows good bonding with the substrate which has flat and smooth interface. The thickness of the coating is about 136 μm, which slows down the oxidation of the substrate and plays an effective role in protecting the substrate.Pei FengYuhua ShiPeng ShangHanjun WeiTongtong PengLisha PangRongrong FengWenyuan ZhangMDPI AGarticleNi–W graded coatingsbackward propagation (BP) neural networkpulse electrodepositionhigh temperature oxidationTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6781, p 6781 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ni–W graded coatings backward propagation (BP) neural network pulse electrodeposition high temperature oxidation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	Ni–W graded coatings backward propagation (BP) neural network pulse electrodeposition high temperature oxidation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Pei Feng Yuhua Shi Peng Shang Hanjun Wei Tongtong Peng Lisha Pang Rongrong Feng Wenyuan Zhang Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
description	The internal stress difference between soft-ductile aluminum alloy substrate and hard-brittle Ni–W alloy coating will cause stress concentration, thus leading to the problem of poor bonding force. Herein, this work prepared the Ni–W graded coating on aluminum alloy matrix by the pulse electrodeposition method in order to solve the mechanical mismatch problem between substrate and coatings. More importantly, a backward propagation (BP) neural network was applied to efficiently optimize the pulse electrodeposition process of Ni–W graded coating. The SEM, EDS, XRD, Vickers hardness tester and Weighing scales are used to analyze the micromorphology, chemical element, phase composition, and micro hardness as well as oxidation weight increase, respectively. The results show that the optimal process conditions with BP neural network are as follows: the bath temperature is 30 °C, current density is 15 mA/cm<sup>2</sup> and duty cycle is 0.3. The predicted value of the model agrees well with the experimental value curve, the relative error is minor. The maximum error is less than 3%, and the correlation coefficient is 0.9996. The Ni–W graded coating prepared by BP neural network shows good bonding with the substrate which has flat and smooth interface. The thickness of the coating is about 136 μm, which slows down the oxidation of the substrate and plays an effective role in protecting the substrate.
format	article
author	Pei Feng Yuhua Shi Peng Shang Hanjun Wei Tongtong Peng Lisha Pang Rongrong Feng Wenyuan Zhang
author_facet	Pei Feng Yuhua Shi Peng Shang Hanjun Wei Tongtong Peng Lisha Pang Rongrong Feng Wenyuan Zhang
author_sort	Pei Feng
title	Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
title_short	Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
title_full	Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
title_fullStr	Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
title_full_unstemmed	Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings
title_sort	application of bp artificial neural network in preparation of ni–w graded coatings
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4e1033900cfd4b0280bd98ae2acd9931
work_keys_str_mv	AT peifeng applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT yuhuashi applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT pengshang applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT hanjunwei applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT tongtongpeng applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT lishapang applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT rongrongfeng applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings AT wenyuanzhang applicationofbpartificialneuralnetworkinpreparationofniwgradedcoatings
_version_	1718411435899355136

Application of BP Artificial Neural Network in Preparation of Ni–W Graded Coatings

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