Botha, NatashaInglis, HMCoetzer, RLabuschagne, FJWJ2021-12-062021-12-062021-12Botha, N., Inglis, H., Coetzer, R. & Labuschagne, F. 2021. Statistical design of experiments: An introductory case study for polymer composites manufacturing applications. http://hdl.handle.net/10204/12191 .2261-236Xhttp://hdl.handle.net/10204/12191Statistical 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.FulltextenBox-Behnken DesignCentral composite designD-optimal designDesign of experimentsDoEExperimental planningOptimal designsPolymer compositesTaguchi DesignConference PresentationBotha, N., Inglis, H., Coetzer, R., & Labuschagne, F. (2021). Statistical design of experiments: An introductory case study for polymer composites manufacturing applications. http://hdl.handle.net/10204/12191Botha, Natasha, HM Inglis, R Coetzer, and FJWJ Labuschagne. "Statistical design of experiments: An introductory case study for polymer composites manufacturing applications." <i>12th South African Conference on Computational and Applied Mechanics (SACAM2020), Cape Town, South Africa, 29 November - 1 December 2021</i> (2021): http://hdl.handle.net/10204/12191Botha N, Inglis H, Coetzer R, Labuschagne F, Statistical design of experiments: An introductory case study for polymer composites manufacturing applications; 2021. http://hdl.handle.net/10204/12191 .TY - Conference Presentation AU - Botha, Natasha AU - Inglis, HM AU - Coetzer, R AU - Labuschagne, FJWJ AB - 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. DA - 2021-12 DB - ResearchSpace DP - CSIR J1 - 12th South African Conference on Computational and Applied Mechanics (SACAM2020), Cape Town, South Africa, 29 November - 1 December 2021 KW - Box-Behnken Design KW - Central composite design KW - D-optimal design KW - Design of experiments KW - DoE KW - Experimental planning KW - Optimal designs KW - Polymer composites KW - Taguchi Design LK - https://researchspace.csir.co.za PY - 2021 SM - 2261-236X T1 - Statistical design of experiments: An introductory case study for polymer composites manufacturing applications TI - Statistical design of experiments: An introductory case study for polymer composites manufacturing applications UR - http://hdl.handle.net/10204/12191 ER -25174