Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing

The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe...

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Autores principales:	Xuhui Xia, Mingjian Gong, Tong Wang, Yubo Liu, Huan Zhang, Zelin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	inverse analysis particle swarm optimization algorithm Y-U hardening model remanufacturing Mining engineering. Metallurgy TN1-997
Acceso en línea:	https://doaj.org/article/43808c9d7cf64183a3fd7a550db3fe03
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spelling	oai:doaj.org-article:43808c9d7cf64183a3fd7a550db3fe032021-11-25T18:22:28ZParameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing10.3390/met111118592075-4701https://doaj.org/article/43808c9d7cf64183a3fd7a550db3fe032021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4701/11/11/1859https://doaj.org/toc/2075-4701The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe the stress-strain relationship for remanufacturing, in particular the cyclic deformation of parts, by using the particle swarm optimization (PSO) method to acquire the Yoshida-Uemori (Y-U) hardening model parameters. To achieve this, tension-compression experimental data of AA7075-O, standard PSO, oscillating second-order PSO (OS-PSO) and variable weight PSO (VW-PSO) were acquired separately. The influence of particle numbers on the inverse analysis efficiency was studied based on standard PSO. Comparing the results of PSO variations showed that: (1) standard PSO is able to avoid local solutions and obtain Y-U model parameters to the same degree of precision as the OS-PSO; (2) by adjusting section weight, the VW-PSO could improve local fitting accuracy and adapt to asymmetric deformation; (3) by reducing particle numbers to a certain extent, the efficiency of analysis can be improved while also maintaining accuracy.Xuhui XiaMingjian GongTong WangYubo LiuHuan ZhangZelin ZhangMDPI AGarticleinverse analysisparticle swarm optimization algorithmY-U hardening modelremanufacturingMining engineering. MetallurgyTN1-997ENMetals, Vol 11, Iss 1859, p 1859 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	inverse analysis particle swarm optimization algorithm Y-U hardening model remanufacturing Mining engineering. Metallurgy TN1-997
spellingShingle	inverse analysis particle swarm optimization algorithm Y-U hardening model remanufacturing Mining engineering. Metallurgy TN1-997 Xuhui Xia Mingjian Gong Tong Wang Yubo Liu Huan Zhang Zelin Zhang Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
description	The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe the stress-strain relationship for remanufacturing, in particular the cyclic deformation of parts, by using the particle swarm optimization (PSO) method to acquire the Yoshida-Uemori (Y-U) hardening model parameters. To achieve this, tension-compression experimental data of AA7075-O, standard PSO, oscillating second-order PSO (OS-PSO) and variable weight PSO (VW-PSO) were acquired separately. The influence of particle numbers on the inverse analysis efficiency was studied based on standard PSO. Comparing the results of PSO variations showed that: (1) standard PSO is able to avoid local solutions and obtain Y-U model parameters to the same degree of precision as the OS-PSO; (2) by adjusting section weight, the VW-PSO could improve local fitting accuracy and adapt to asymmetric deformation; (3) by reducing particle numbers to a certain extent, the efficiency of analysis can be improved while also maintaining accuracy.
format	article
author	Xuhui Xia Mingjian Gong Tong Wang Yubo Liu Huan Zhang Zelin Zhang
author_facet	Xuhui Xia Mingjian Gong Tong Wang Yubo Liu Huan Zhang Zelin Zhang
author_sort	Xuhui Xia
title	Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
title_short	Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
title_full	Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
title_fullStr	Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
title_full_unstemmed	Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing
title_sort	parameter identification of the yoshida-uemori hardening model for remanufacturing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/43808c9d7cf64183a3fd7a550db3fe03
work_keys_str_mv	AT xuhuixia parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing AT mingjiangong parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing AT tongwang parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing AT yuboliu parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing AT huanzhang parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing AT zelinzhang parameteridentificationoftheyoshidauemorihardeningmodelforremanufacturing
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Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing

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