Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while th...

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Main Authors: Yuxuan Wu, Hanyang Xie, Jyun-You Chiang, Gang Peng, Yan Qin
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2021/9175170
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spelling doaj-8615d83c72ff48ba862bb7df56da8e602021-10-04T01:57:53ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/9175170Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass FiberYuxuan Wu0Hanyang Xie1Jyun-You Chiang2Gang Peng3Yan Qin4School of StatisticsSchool of StatisticsSchool of StatisticsSchool of StatisticsSchool of Foreign Languages for BusinessGlass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.http://dx.doi.org/10.1155/2021/9175170
collection DOAJ
language English
format Article
sources DOAJ
author Yuxuan Wu
Hanyang Xie
Jyun-You Chiang
Gang Peng
Yan Qin
spellingShingle Yuxuan Wu
Hanyang Xie
Jyun-You Chiang
Gang Peng
Yan Qin
Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
Mathematical Problems in Engineering
author_facet Yuxuan Wu
Hanyang Xie
Jyun-You Chiang
Gang Peng
Yan Qin
author_sort Yuxuan Wu
title Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
title_short Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
title_full Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
title_fullStr Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
title_full_unstemmed Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber
title_sort parameter estimation and applications of the weibull distribution for strength data of glass fiber
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1563-5147
publishDate 2021-01-01
description Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.
url http://dx.doi.org/10.1155/2021/9175170
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