Optimization of running-in surface morphology parameters based on the AutoML model.

Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for runn...

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Autores principales:	Guangyuan Ge, Fenfen Liu, Gengpei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/55844fabb77f4f7690a33eecd75facba
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spelling	oai:doaj.org-article:55844fabb77f4f7690a33eecd75facba2021-12-02T20:17:22ZOptimization of running-in surface morphology parameters based on the AutoML model.1932-620310.1371/journal.pone.0257850https://doaj.org/article/55844fabb77f4f7690a33eecd75facba2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257850https://doaj.org/toc/1932-6203Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing, and the surface morphology optimization is explored. When better oil storage capacity is required, the recommendation is to increase the Sq, Sdq and Sk surface parameter values while setting medium Sdc and Sdr surface parameter values. When a lower coefficient of friction (COF) is required, Sdc and Sdr should be decreased, and Sq and Sdq should be increased. When better support performance is required, Sdc, Sdq, and Sdr should be increased. This article provides a solution to establish a link between surface design and functional performance in the steady wear stage, further filling the gap in quality monitoring of lifecycles.Guangyuan GeFenfen LiuGengpei ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257850 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guangyuan Ge Fenfen Liu Gengpei Zhang Optimization of running-in surface morphology parameters based on the AutoML model.
description	Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing, and the surface morphology optimization is explored. When better oil storage capacity is required, the recommendation is to increase the Sq, Sdq and Sk surface parameter values while setting medium Sdc and Sdr surface parameter values. When a lower coefficient of friction (COF) is required, Sdc and Sdr should be decreased, and Sq and Sdq should be increased. When better support performance is required, Sdc, Sdq, and Sdr should be increased. This article provides a solution to establish a link between surface design and functional performance in the steady wear stage, further filling the gap in quality monitoring of lifecycles.
format	article
author	Guangyuan Ge Fenfen Liu Gengpei Zhang
author_facet	Guangyuan Ge Fenfen Liu Gengpei Zhang
author_sort	Guangyuan Ge
title	Optimization of running-in surface morphology parameters based on the AutoML model.
title_short	Optimization of running-in surface morphology parameters based on the AutoML model.
title_full	Optimization of running-in surface morphology parameters based on the AutoML model.
title_fullStr	Optimization of running-in surface morphology parameters based on the AutoML model.
title_full_unstemmed	Optimization of running-in surface morphology parameters based on the AutoML model.
title_sort	optimization of running-in surface morphology parameters based on the automl model.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/55844fabb77f4f7690a33eecd75facba
work_keys_str_mv	AT guangyuange optimizationofrunninginsurfacemorphologyparametersbasedontheautomlmodel AT fenfenliu optimizationofrunninginsurfacemorphologyparametersbasedontheautomlmodel AT gengpeizhang optimizationofrunninginsurfacemorphologyparametersbasedontheautomlmodel
_version_	1718374406151995392

Optimization of running-in surface morphology parameters based on the AutoML model.

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