Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran

Sanitary porcelain products might have several defects, causing potential high-grade desirable products be converted into low-grade ones. Some of the defects are such that a few of them in the products will result in a great fall of product rating and consequently reduction of its value and price. A...

Full description

Bibliographic Details
Main Authors: H. Bazargan, A. Dehghanzadeh
Format: Article
Language:English
Published: Atlantis Press 2018-09-01
Series:Journal of Statistical Theory and Applications (JSTA)
Subjects:
Online Access:https://www.atlantis-press.com/article/25903305/view
id doaj-0297e72446c24d31b1db7c3188fbebc2
record_format Article
spelling doaj-0297e72446c24d31b1db7c3188fbebc22020-11-25T02:08:40ZengAtlantis PressJournal of Statistical Theory and Applications (JSTA)1538-78872018-09-0117310.2991/jsta.2018.17.3.11Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, IranH. BazarganA. DehghanzadehSanitary porcelain products might have several defects, causing potential high-grade desirable products be converted into low-grade ones. Some of the defects are such that a few of them in the products will result in a great fall of product rating and consequently reduction of its value and price. Among these defects is a defect called pinhole. In this article, it has been tried to identify, from a list of factors, the most influential factors of the production process which cause the pinhole defect affecting the product rating. It then tries to present a prediction model for the number of pinholes. For this purpose, initially seven factors were chosen to help presenting a suitable prediction model and several statistical tools and artificial intelligence prediction tools were investigated to present a suitable prediction model. The presented model could be used by the company to enhance the product rating through choosing right value for the right factors causing the pinhole defect and to decrease the wastes and the expenditures.https://www.atlantis-press.com/article/25903305/viewsanitary porcelainpinholeartificial neural networksregressioncorrelation coefficient
collection DOAJ
language English
format Article
sources DOAJ
author H. Bazargan
A. Dehghanzadeh
spellingShingle H. Bazargan
A. Dehghanzadeh
Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
Journal of Statistical Theory and Applications (JSTA)
sanitary porcelain
pinhole
artificial neural networks
regression
correlation coefficient
author_facet H. Bazargan
A. Dehghanzadeh
author_sort H. Bazargan
title Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
title_short Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
title_full Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
title_fullStr Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
title_full_unstemmed Modeling Pinhole Phenomenon in Sanitary Porcelains Case Study: Isatis Sanitary Porcelain Plant, Yazd, Iran
title_sort modeling pinhole phenomenon in sanitary porcelains case study: isatis sanitary porcelain plant, yazd, iran
publisher Atlantis Press
series Journal of Statistical Theory and Applications (JSTA)
issn 1538-7887
publishDate 2018-09-01
description Sanitary porcelain products might have several defects, causing potential high-grade desirable products be converted into low-grade ones. Some of the defects are such that a few of them in the products will result in a great fall of product rating and consequently reduction of its value and price. Among these defects is a defect called pinhole. In this article, it has been tried to identify, from a list of factors, the most influential factors of the production process which cause the pinhole defect affecting the product rating. It then tries to present a prediction model for the number of pinholes. For this purpose, initially seven factors were chosen to help presenting a suitable prediction model and several statistical tools and artificial intelligence prediction tools were investigated to present a suitable prediction model. The presented model could be used by the company to enhance the product rating through choosing right value for the right factors causing the pinhole defect and to decrease the wastes and the expenditures.
topic sanitary porcelain
pinhole
artificial neural networks
regression
correlation coefficient
url https://www.atlantis-press.com/article/25903305/view
work_keys_str_mv AT hbazargan modelingpinholephenomenoninsanitaryporcelainscasestudyisatissanitaryporcelainplantyazdiran
AT adehghanzadeh modelingpinholephenomenoninsanitaryporcelainscasestudyisatissanitaryporcelainplantyazdiran
_version_ 1724926127617605632