Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network

The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yutao Li, Kaiming Wang, Hanguang Fu, Xiaohui Zhi, Xingye Guo, Jian Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	laser cladding AlCoCrFeNi coatings BP neural network dilution rate Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/006718510e934ea69831ce7560bd2afb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:006718510e934ea69831ce7560bd2afb
record_format	dspace
spelling	oai:doaj.org-article:006718510e934ea69831ce7560bd2afb2021-11-25T17:16:52ZPrediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network10.3390/coatings111114022079-6412https://doaj.org/article/006718510e934ea69831ce7560bd2afb2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-6412/11/11/1402https://doaj.org/toc/2079-6412The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV<sub>0.3</sub>. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.Yutao LiKaiming WangHanguang FuXiaohui ZhiXingye GuoJian LinMDPI AGarticlelaser claddingAlCoCrFeNi coatingsBP neural networkdilution rateEngineering (General). Civil engineering (General)TA1-2040ENCoatings, Vol 11, Iss 1402, p 1402 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	laser cladding AlCoCrFeNi coatings BP neural network dilution rate Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	laser cladding AlCoCrFeNi coatings BP neural network dilution rate Engineering (General). Civil engineering (General) TA1-2040 Yutao Li Kaiming Wang Hanguang Fu Xiaohui Zhi Xingye Guo Jian Lin Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
description	The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV<sub>0.3</sub>. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.
format	article
author	Yutao Li Kaiming Wang Hanguang Fu Xiaohui Zhi Xingye Guo Jian Lin
author_facet	Yutao Li Kaiming Wang Hanguang Fu Xiaohui Zhi Xingye Guo Jian Lin
author_sort	Yutao Li
title	Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
title_short	Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
title_full	Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
title_fullStr	Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
title_full_unstemmed	Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
title_sort	prediction for dilution rate of alcocrfeni coatings by laser cladding based on a bp neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/006718510e934ea69831ce7560bd2afb
work_keys_str_mv	AT yutaoli predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork AT kaimingwang predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork AT hanguangfu predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork AT xiaohuizhi predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork AT xingyeguo predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork AT jianlin predictionfordilutionrateofalcocrfenicoatingsbylasercladdingbasedonabpneuralnetwork
_version_	1718412508317876224

Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network

Ejemplares similares