Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the...

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Autores principales:	Botha Natasha, Inglis Helen M., Coetzer Roelof, Labuschagne F. Johan W.J.
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/989c4b77628f4c0f8211e06ce9fcfa74
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spelling	oai:doaj.org-article:989c4b77628f4c0f8211e06ce9fcfa742021-12-02T17:13:35ZStatistical Design of Experiments: An introductory case study for polymer composites manufacturing applications2261-236X10.1051/matecconf/202134700028https://doaj.org/article/989c4b77628f4c0f8211e06ce9fcfa742021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/16/matecconf_sacam21_00028.pdfhttps://doaj.org/toc/2261-236XStatistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.Botha NatashaInglis Helen M.Coetzer RoelofLabuschagne F. Johan W.J.EDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 347, p 00028 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Botha Natasha Inglis Helen M. Coetzer Roelof Labuschagne F. Johan W.J. Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
description	Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.
format	article
author	Botha Natasha Inglis Helen M. Coetzer Roelof Labuschagne F. Johan W.J.
author_facet	Botha Natasha Inglis Helen M. Coetzer Roelof Labuschagne F. Johan W.J.
author_sort	Botha Natasha
title	Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
title_short	Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
title_full	Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
title_fullStr	Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
title_full_unstemmed	Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications
title_sort	statistical design of experiments: an introductory case study for polymer composites manufacturing applications
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/989c4b77628f4c0f8211e06ce9fcfa74
work_keys_str_mv	AT bothanatasha statisticaldesignofexperimentsanintroductorycasestudyforpolymercompositesmanufacturingapplications AT inglishelenm statisticaldesignofexperimentsanintroductorycasestudyforpolymercompositesmanufacturingapplications AT coetzerroelof statisticaldesignofexperimentsanintroductorycasestudyforpolymercompositesmanufacturingapplications AT labuschagnefjohanwj statisticaldesignofexperimentsanintroductorycasestudyforpolymercompositesmanufacturingapplications
_version_	1718381333069168640

Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications

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