Use of optimal mixture‐process designs and response‐surface models to study properties of calcium silicate units

[1]  Linda M. Haines,et al.  Optimal Designs for Multiple-Mixture by Process Variable Experiments , 2013 .

[2]  Peter Goos,et al.  I-Optimal Design of Mixture Experiments , 2016 .

[3]  Wanida Limmun,et al.  Using a Genetic Algorithm to Generate D‐optimal Designs for Mixture Experiments , 2013, Qual. Reliab. Eng. Int..

[4]  Connie M. Borror,et al.  Fraction of Design Space Plots for Assessing Mixture and Mixture-Process Designs , 2004 .

[5]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[6]  H. Akaike A new look at the statistical model identification , 1974 .

[7]  Connie M. Borror,et al.  Three-Dimensional Variance Dispersion Graphs for Mixture-Process Experiments , 2004 .

[8]  R. H. Myers,et al.  Graphical assessment of the prediction capability of response surface designs , 1989 .

[9]  David M. Allen,et al.  The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .

[10]  P. Goos,et al.  I-Optimal Design of Mixture Experiments in the Presence of Ingredient Availability Constraints , 2015 .

[11]  Scott M. Kowalski,et al.  A new model and class of designs for mixture experiments with process variables , 2000 .

[12]  M. Agha,et al.  Experiments with Mixtures , 1992 .

[13]  Hanen Hanna,et al.  A new class of designs for mixture-of-mixture experiments , 2015 .

[14]  R. H. Myers,et al.  Fraction of Design Space to Assess Prediction Capability of Response Surface Designs , 2003 .

[15]  R. K. Meyer,et al.  The Coordinate-Exchange Algorithm for Constructing Exact Optimal Experimental Designs , 1995 .