A Note on Small Composite Designs for Sequential Experimentation

Nguyen, Nam-Ky; Lin, Dennis K.J.

doi:10.1080/15598608.2011.10412054

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Then, the rounded Dvalues are given and in parentheses the total run sizes of the composite designs. Also, the proposed designs are compared with the existing composite designs in [13], [16], [15], [10], [26], and [19].…”

Section: Resultsmentioning

confidence: 99%

“…CCDs with a minimum number of runs for 7, 8, and 9 factors were constructed in [15]. Other small composite designs are provided in [16]. The previous mentioned designs were constructed by focusing on factorial part to reduce the number of runs and they used the classical axial part.…”

Section: Introductionmentioning

confidence: 99%

“…All the existing composite designs in the literature, with odd number of factors, have either the classical axial part (see [10]- [16]) or an axial part that maintain only one or two of the following properties: a) minimum number of runs and b) orthogonality and c) highest D-value (see [18] [19]). This paper aims at developing a method for constructing designs that solve this issue and generating designs that simultaneously posses all the three desirable properties a), b) and c).…”

Section: Introductionmentioning

confidence: 99%

“…This paper aims at developing a method for constructing designs that solve this issue and generating designs that simultaneously posses all the three desirable properties a), b) and c). The designs that succesfully posses all the three properties could exist for odd orders only ( see [15], [16], and [19]). This situation strongly motivated this research and this investigation for designs that can simultaneously have all the three desirable properties and at the same time be easy to construct and use.…”

Section: Introductionmentioning

confidence: 99%

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A New Class of Second-Order Response Surface Designs

2020

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Response surface methodology (RSM) refers to experimental designs for optimizing or developing processes, initially in manufacturing. In this paper, a new method is presented and an algorithm is implemented that modifies the axial part in a central composite design to achieve a good D-value and efficiency. The new designs are suitable for sequential experimentation. In comparison with known designs in the same class, the new designs are tested and found to have better D-values on a range of factors. With this new approach, efficient orthogonal designs for response surface methodology were generated for a number of parameters that were previously impossible to construct. The new generated designs and their comparison with known designs from the literature are presented in tables for practitioners' use.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Class of Second-Order Response Surface Designs

2020

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“…In this study, the following designs were considered: Full factorial designs (FD); Fractional factorial designs (FFD); Plackett‐Burman designs (PBD); Central composite designs (CCD); Box‐Behnken designs (BBD); Optimal designs (D‐criterion); Definitive screening designs (DSD) For the sake of space, this study does not cover all available designs, excluding, for instance, small composite designs, several supersaturated designs, and other types of optimal designs . For more information on these designs, the interested readers are referred to the vast literature in the field …”

Section: Designs Contemplated In This Studymentioning

confidence: 99%

Design of Experiments: A comparison study from the non‐expert user's perspective

Santos

Rato

Reis

2018

Journal of Chemometrics

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In a digital era where terabytes of structured and unstructured records are created and stored every minute, the importance of collecting small amounts of high quality data is often undervalued. However, this activity plays a critical role in industrial and laboratory settings, when addressing problems from process modeling and analysis, to optimization and robust design. Implementing a screening design is usually the way to begin a systematic statistical Design of Experiments program. Its aim is to find the influential factors and establish a first description of the process under analysis. Recently, new developments have emerged in this field, especially with the appearance of a new class of designs that join screening efficiency with the possibility to estimate quadratic effects (definitive screening designs). Therefore, several alternatives are currently available for conducting screening studies, but information is still scarce on the pros and cons of each methodology. This study was thus designed to gather useful information from the user's perspective by independently considering each design with rather standard settings. For each design, its ability to recover the correct model structure was evaluated, through Monte Carlo simulations on several simulated process structures containing elements likely to be found in practice (they obey the general principles of effect sparsity, hierarchy, and heredity). As some screening designs can estimate quadratic terms, these effects were also considered in the simulated models, and three-level designs were brought to the analysis for comparison purposes, even though they are not screening designs.

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