Comparisons with the best in response surface methodology

Moore, Linda J.; Sa, Ping

doi:10.1016/s0167-7152(99)00008-5

Cited by 9 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RSM is a model-building research method that incorporates statistics [19][20][21][22]. It uses appropriate polynomial models to approximate the relationship between the response variable of a problem and several independent variables.…”

Section: Response Surface Methodologymentioning

confidence: 99%

Determination of injection molding process windows for optical lenses using response surface methodology

Tsai

Wang

2014

Appl. Opt.

View full text Add to dashboard Cite

This study focuses on injection molding process window determination for obtaining optimal imaging optical properties, astigmatism, coma, and spherical aberration using plastic lenses. The Taguchi experimental method was first used to identify the optimized combination of parameters and significant factors affecting the imaging optical properties of the lens. Full factorial experiments were then implemented based on the significant factors to build the response surface models. The injection molding process windows for lenses with optimized optical properties were determined based on the surface models, and confirmation experiments were performed to verify their validity. The results indicated that the significant factors affecting the optical properties of lenses are mold temperature, melt temperature, and cooling time. According to experimental data for the significant factors, the oblique ovals for different optical properties on the injection molding process windows based on melt temperature and cooling time can be obtained using the curve fitting approach. The confirmation experiments revealed that the average errors for astigmatism, coma, and spherical aberration are 3.44%, 5.62%, and 5.69%, respectively. The results indicated that the process windows proposed are highly reliable.

show abstract

Section: Response Surface Methodologymentioning

confidence: 99%

Determination of injection molding process windows for optical lenses using response surface methodology

Tsai

Wang

2014

Appl. Opt.

View full text Add to dashboard Cite

show abstract

“…Sampling from the uniform distribution on the n-dimensional Euclidean ball (the n-ball) and its surface (the n-sphere) is a tool useful in many diverse research fields, for instance Monte Carlo integration in physics, generating random directions for MCMC sampling on bounded regions, generating random correlation matrices, Monte Carlo analysis of random packing on the sphere, generating random rotations in cryptography as well as various simulation studies in statistics (see, e.g., [8], Section 5.13 in [12], [20,26,28,22]). …”

Section: Introductionmentioning

confidence: 99%

On decompositional algorithms for uniform sampling from n-spheres and n-balls

Harman

Lacko

2010

Journal of Multivariate Analysis

View full text Add to dashboard Cite

a b s t r a c tWe describe a universal conditional distribution method for uniform sampling from nspheres and n-balls, based on properties of a family of radially symmetric multivariate distributions. The method provides us with a unifying view on several known algorithms as well as enabling us to construct novel variants. We give a numerical comparison of the known and newly proposed algorithms for dimensions 5, 6 and 7.

show abstract

“…Results of an efficiency study to compare the new method and the existing method by Moore and Sa (1999) are provided. …”

mentioning

confidence: 99%

“…The MCB problem was first approached by Hsu (1984) in design of experiments where he considered the problem of comparing the treatment means under study with the "best" treatment mean. Moore and Sa (1999) first approached the MCB problem in the RSM setting. There has also been other substantial work on related problems within the field of response surface methodology.…”

mentioning

confidence: 99%