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Journal articles on the topic 'Response surface design'

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Published: 4 June 2021
Last updated: 4 February 2022

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1

Donnelly, T. A. "Response-surface experimental design." IEEE Potentials 11, no. 1 (February 1992): 19–21. http://dx.doi.org/10.1109/45.127696.

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2

Fearn, Tom. "Design of Experiments 5: Response Surface Designs." NIR news 18, no. 7 (November 2007): 14–15. http://dx.doi.org/10.1255/nirn.1048.

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3

Anderson-Cook, Christine M., Connie M. Borror, and Douglas C. Montgomery. "Response surface design evaluation and comparison." Journal of Statistical Planning and Inference 139, no. 2 (February 2009): 629–41. http://dx.doi.org/10.1016/j.jspi.2008.04.004.

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4

Yoon, Jong-Hwan. "Optimum Design of Surface Aerator Using Response Surface Method." Journal of the Korean Society of Visualization 7, no. 2 (January 8, 2010): 47–55. http://dx.doi.org/10.5407/jksv.2010.7.2.047.

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5

Jeang, Angus. "Optimal tolerance design by response surface methodology." International Journal of Production Research 37, no. 14 (September 1999): 3275–88. http://dx.doi.org/10.1080/002075499190284.

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6

Morgenthaler, Stephan, and Martin M. Schumacher. "Robust analysis of a response surface design." Chemometrics and Intelligent Laboratory Systems 47, no. 1 (April 1999): 127–41. http://dx.doi.org/10.1016/s0169-7439(98)00199-3.

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7

Freek Huele, A., and Jan Engel. "A response surface approach to tolerance design." Statistica Neerlandica 60, no. 3 (August 2006): 379–95. http://dx.doi.org/10.1111/j.1467-9574.2006.00332.x.

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8

Vaughan, Timothy S. "Experimental design for response surface gradient estimation." Communications in Statistics - Theory and Methods 22, no. 6 (January 1993): 1535–55. http://dx.doi.org/10.1080/03610929308831102.

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9

Khattree, Ravindra. "Robust Parameter Design: A Response Surface Approach." Journal of Quality Technology 28, no. 2 (April 1996): 187–98. http://dx.doi.org/10.1080/00224065.1996.11979659.

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10

Ranade, Shruti Sunil, and Padma Thiagarajan. "Selection of a design for response surface." IOP Conference Series: Materials Science and Engineering 263 (November 2017): 022043. http://dx.doi.org/10.1088/1757-899x/263/2/022043.

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11

Jeang, A. "Robust Tolerance Design by Response Surface Methodology." International Journal of Advanced Manufacturing Technology 15, no. 6 (June 14, 1999): 399–403. http://dx.doi.org/10.1007/s001700050082.

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12

Wang, Qiu, Zhi Gang Song, and Jing Cao. "Slope Optimization Design Based on Improved Response Surface." Applied Mechanics and Materials 170-173 (May 2012): 723–28. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.723.

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Due to lacking of explicitly analytical model, optimization design of complex slope is often consulted to enumerative algorithm which generally results huge computation efforts. To overcome this problem, a new optimization method based on improved response surface (IRS) is proposed. The IRS, constructed by uniform design (UD) and non-parametric regression (NR), provides a regressed and explicitly analytical model through a few trial computations without prior assumed sliding surface. The optimization process is explained and a slope is optimized to verify the feasibility of the proposed method. The results show that the provided method can optimize a slope with multiple sliding surfaces and provide an optimum result under the safety constraints of the slope. Meanwhile, the introduction of UD and NR to construct the IRS can provide a better regression effect with fewer computational costs.
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13

Balabanov, Vladimir O., Antony A. Giunta, Oleg Golovidov, Bernard Grossman, William H. Mason, Layne T. Watson, and Raphael T. Haftka. "Reasonable Design Space Approach to Response Surface Approximation." Journal of Aircraft 36, no. 1 (January 1999): 308–15. http://dx.doi.org/10.2514/2.2438.

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14

Venter, Gerhard, Raphael T. Haftka, and James H. Starnes. "Construction of Response Surface Approximations for Design Optimization." AIAA Journal 36, no. 12 (December 1998): 2242–49. http://dx.doi.org/10.2514/2.333.

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15

Luo Jia-Qi and Liu Feng. "Gradient-based response surface approximations for design optimization." Acta Physica Sinica 62, no. 19 (2013): 190201. http://dx.doi.org/10.7498/aps.62.190201.

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16

Anderson-Cook, Christine M., Connie M. Borror, and Douglas C. Montgomery. "Rejoinder for “Response surface design evaluation and comparison”." Journal of Statistical Planning and Inference 139, no. 2 (February 2009): 671–74. http://dx.doi.org/10.1016/j.jspi.2008.04.009.

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17

Goos, Peter. "Discussion of “Response surface design evaluation and comparison”." Journal of Statistical Planning and Inference 139, no. 2 (February 2009): 657–59. http://dx.doi.org/10.1016/j.jspi.2008.04.012.

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18

Lehký, David, and Martina Šomodíková. "Inverse Response Surface Method in Reliability-Based Design." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. 17, no. 2 (December 1, 2017): 37–42. http://dx.doi.org/10.1515/tvsb-2017-0025.

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Abstract The paper introduces an inverse response surface method utilized when performing reliability-based design optimization of time-consuming problems. Proposed procedure is based on a coupling of the adaptive response surface method and the artificial neural network-based inverse reliability method. The validity and accuracy of the method is tested using examples with explicit nonlinear limit state functions. Obtained results as well as important aspects of the method are discussed.
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19

Mitchell, Toby, Jerome Sacks, and Donald Ylvisaker. "Asymptotic Bayes Criteria for Nonparametric Response Surface Design." Annals of Statistics 22, no. 2 (June 1994): 634–51. http://dx.doi.org/10.1214/aos/1176325488.

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20

Venter, Gerhard, Raphael T. Haftka, and James H. Starnes. "Construction of response surface approximations for design optimization." AIAA Journal 36 (January 1998): 2242–49. http://dx.doi.org/10.2514/3.14111.

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21

Xin, Luo, Xu Jinyu, and Li Weimin. "Response surface design of solid waste based geopolymer." RSC Advances 5, no. 2 (2015): 1598–604. http://dx.doi.org/10.1039/c4ra05458j.

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Solid waste (slag and fly ash) based geopolymer (SWG-SF) was developed on the basis of response surface methodology (RSM), using the fly ash to slag ratio (FSR), alkali content (AC), makeup of alkali activator (n) and binder to water ratio (BWR) as design parameters.
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22

Jordaan, J. P., and C. P. Ungerer. "Optimization of Design Tolerances Through Response Surface Approximations." Journal of Manufacturing Science and Engineering 124, no. 3 (July 11, 2002): 762–67. http://dx.doi.org/10.1115/1.1381400.

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A methodology whereby the optimal set of design tolerances is assigned to the dimensions of a general mechanical assembly, is developed and tested. The manufacturing cost is minimized, while the design is constrained to a specified probability of meeting functional requirements, called the yield of the design. An analytical relationship for the assembly yield surface is generally unknown, and use is made of response surface approximations in the optimization algorithm. Yield values are determined at design space points through Monte Carlo simulations, seen as the response surface experiments. The methodology is benchmarked on example problems from the literature, and the optimum compares superior to published results.
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23

Kurtaran, H., A. Eskandarian, D. Marzougui, and N. E. Bedewi. "Crashworthiness design optimization using successive response surface approximations." Computational Mechanics 29, no. 4-5 (October 1, 2002): 409–21. http://dx.doi.org/10.1007/s00466-002-0351-x.

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24

Sun, Hyosung. "Wind turbine airfoil design using response surface method." Journal of Mechanical Science and Technology 25, no. 5 (May 2011): 1335–40. http://dx.doi.org/10.1007/s12206-011-0310-6.

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25

Atkinson, Anthony C. "Optimum and other response surface designs. Comments on “Response Surface Design Evaluation and Comparison” by Anderson-Cook, Borror and Montgomery." Journal of Statistical Planning and Inference 139, no. 2 (February 2009): 662–68. http://dx.doi.org/10.1016/j.jspi.2008.04.014.

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26

Zahran, Alyaa, Christine M. Anderson-Cook, and Raymond H. Myers. "Fraction of Design Space to Assess Prediction Capability of Response Surface Designs." Journal of Quality Technology 35, no. 4 (October 2003): 377–86. http://dx.doi.org/10.1080/00224065.2003.11980235.

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27

van Driel, W. D., G. Q. Zhang, J. H. J. Janssen, and L. J. Ernst. "Response Surface Modeling for Nonlinear Packaging Stresses." Journal of Electronic Packaging 125, no. 4 (December 1, 2003): 490–97. http://dx.doi.org/10.1115/1.1604149.

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The present study focuses on the development of reliable response surface models (RSM’s) for the major packaging processes of a typical electronic package. The major objective is to optimize the product/process designs against the possible failure mode of vertical die cracks. First, the finite element mode (FEM)-based physics of failure models are developed and the reliability of the predicted stress levels was verified by experiments. In the development of reliable thermo-mechanical simulation models, both the process (time and temperature) dependent material nonlinearity and geometric nonlinearity are taken into account. Afterwards, RSM’s covering the whole specified geometric design spaces are constructed. Finally, these RSM’s are used to predict, evaluate, optimize, and eventually qualify the thermo-mechanical behavior of this electronic package against the actual design requirements prior to major physical prototyping and manufacturing investments.
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28

Iwundu, Mary Paschal. "Alternative Second-Order N-Point Spherical Response Surface Methodology Design and Their Efficiencies." International Journal of Statistics and Probability 5, no. 4 (June 11, 2016): 22. http://dx.doi.org/10.5539/ijsp.v5n4p22.

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The equiradial designs are studied as alternative second-order N-point spherical Response Surface Methodology designs in two variables, for design radius ρ = 1.0. These designs are seen comparable with the standard second-order response surface methodology designs, namely the Central Composite Designs. The D-efficiencies of the equiradial designs are evaluated with respect to the spherical Central Composite Designs. Furthermore, D-efficiencies of the equiradial designs are evaluated with respect to the D-optimal exact designs defined on the design regions of the Circumscribed Central Composite Design, the Inscribed Central Composite Design and the Face-centered Central Composite Design. The D-efficiency values reveal that the alternative second-order N-point spherical equiradial designs are better than the Inscribed Central Composite Design though inferior to the Circumscribed Central Composite Design with efficiency values less than 50% in all cases studied. Also, D-efficiency values reveal that the alternative second-order N-point spherical equiradial designs are better than the N-point D-optimal exact designs defined on the design region supported by the design points of the Inscribed Central Composite Design. However, the N-point spherical equiradial designs are inferior to the N-point D-optimal exact designs defined on the design region supported by the design points of the Circumscribed Central Composite Design and those of the Face-centered Central Composite Design, with worse cases with respect to the design region of the Circumscribed Central Composite Design.
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29

Matsuura, Shun, Hideo Suzuki, Takahisa Iida, Hirotaka Kure, and Hatsuo Mori. "Robust parameter design using a supersaturated design for a response surface model." Quality and Reliability Engineering International 27, no. 4 (November 18, 2010): 541–54. http://dx.doi.org/10.1002/qre.1160.

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30

Fang, Sheng En, and Ricardo Perera. "Damage Identification Using Response Surface Methodology." Key Engineering Materials 413-414 (June 2009): 669–76. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.669.

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As a combination of statistical and mathematical techniques, response surface methodology gives explicit functions to express the relationship between the inputs and outputs of a physical system. This methodology has been widely applied to design optimization, response prediction and model validation but so far little literature related to its application in structural damage identification has been found. Therefore this paper presents a systematic damage identification procedure consisting of four steps of feature selection, parameter screening, primary response surface modeling and updating, reference-state response surface modeling with damage identification realization. 2k factorial design and central composite design are adopted to construct response surface models for parameter screening and model updating purposes, respectively. The proposed method is verified against an experimental reinforced concrete frame and it is found that the proposed method works well in damage prediction.
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31

Batill, Stephen M., Marc A. Stelmack, and Richard S. Sellar. "Framework for Multidisciplinary Design Based on Response-Surface Approximations." Journal of Aircraft 36, no. 1 (January 1999): 287–97. http://dx.doi.org/10.2514/2.2436.

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32

Ahn, Jaekwon, Hyoung-Jin Kim, Dong-Ho Lee, and Oh-Hyun Rho. "Response Surface Method for Airfoil Design in Transonic Flow." Journal of Aircraft 38, no. 2 (March 2001): 231–38. http://dx.doi.org/10.2514/2.2780.

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33

Nardi, J. V., and Dachamir Hotza. "Mixture Design and Response Surface Analysis of Pozzolanic Products." Materials Science Forum 416-418 (February 2003): 537–42. http://dx.doi.org/10.4028/www.scientific.net/msf.416-418.537.

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34

Wang, Qiu, Zhi Gang Song, and Qing Xu. "Soil Nailing Optimization Design Based on Improved Response Surface." Advanced Materials Research 671-674 (March 2013): 126–32. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.126.

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Gradient algorithm is difficult to obtain explicit analytic function of the optimization model, at the same time heuristic algorithm is computationally intensive with low speed and less efficient in soil nailing optimization. To overcome these problems, a new optimization method based on improved response surface (IRS) which constructed by uniform design (UD) and non-parametric regression (NR), is proposed. The soil nailing optimization is adopted by the combination of explicit analytic model based on IRS and composing program. The optimization process is explained and a soil nailing is optimized to verify the feasibility of the proposed method. The optimum results show that the introduction of UD and NR to construct the IRS calculate fast, do not need solving the specific analytic solution and can obtain global optimal solution.
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35

Perez, Victor M., John E. Renaud, and Layne T. Watson. "Adaptive Experimental Design for Construction of Response Surface Approximations." AIAA Journal 40, no. 12 (December 2002): 2495–503. http://dx.doi.org/10.2514/2.1593.

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36

Mulani, Sameer B., Pankaj Joshi, Jing Li, Rakesh K. Kapania, and Yung Seok Shin. "Optimal Design of Unitized Structures Using Response Surface Approaches." Journal of Aircraft 47, no. 6 (November 2010): 1898–906. http://dx.doi.org/10.2514/1.47411.

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37

Robinson, Timothy J. "A discussion of “Response surface design evaluation and comparison”." Journal of Statistical Planning and Inference 139, no. 2 (February 2009): 669–70. http://dx.doi.org/10.1016/j.jspi.2008.04.010.

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38

Abu-Odeh, Akram Y., and Harry L. Jones. "Optimum design of composite plates using response surface method." Composite Structures 43, no. 3 (November 1998): 233–42. http://dx.doi.org/10.1016/s0263-8223(98)00109-3.

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39

Gao, X. K., T. S. Low, Z. J. Liu, and S. X. Chen. "Robust design for torque optimization using response surface methodology." IEEE Transactions on Magnetics 38, no. 2 (March 2002): 1141–44. http://dx.doi.org/10.1109/20.996292.

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40

Butler, Neil A. "On weighted design optimality criteria and response surface parameterizations." Statistics & Probability Letters 51, no. 1 (January 2001): 41–46. http://dx.doi.org/10.1016/s0167-7152(00)00140-1.

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41

YAMAZAKI, Wataru. "Advanced Kriging response surface approach for efficient design optimization." Transactions of the JSME (in Japanese) 80, no. 818 (2014): TRANS0285. http://dx.doi.org/10.1299/transjsme.2014trans0285.

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42

Pike, Derek J., and Anne M. Hasted. "Experimental design and response surface analysis of pesticide trials." Pesticide Science 19, no. 4 (1987): 297–307. http://dx.doi.org/10.1002/ps.2780190407.

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43

Alaeddini, Adel, Kai Yang, and Alper Murat. "ASRSM: A Sequential Experimental Design for Response Surface Optimization." Quality and Reliability Engineering International 29, no. 2 (February 6, 2012): 241–58. http://dx.doi.org/10.1002/qre.1306.

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44

Aggarwal, M. L., and Anita Bansal. "Robust response surface design for quantitative and qualitative factors." Communications in Statistics - Theory and Methods 27, no. 1 (January 1998): 89–106. http://dx.doi.org/10.1080/03610929808832652.

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45

Hussey, James R., Raymond H. Myers, and Ernest C. Houck. "Correlated Simulation Experiments in First-Order Response Surface Design." Operations Research 35, no. 5 (October 1987): 744–58. http://dx.doi.org/10.1287/opre.35.5.744.

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46

Yamanaka, Takashi, and Keiichi Motoyama. "Suspension Design using Genetic Algorithms and Response Surface Methodology." Proceedings of the 1992 Annual Meeting of JSME/MMD 2000 (2000): 728. http://dx.doi.org/10.1299/jsmezairiki.2000.0_728.

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47

Möller, Oscar, and Ricardo O. Foschi. "Reliability Evaluation in Seismic Design: A Response Surface Methodology." Earthquake Spectra 19, no. 3 (August 2003): 579–603. http://dx.doi.org/10.1193/1.1598200.

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Performance-based seismic design requires consideration of the uncertainties in corresponding demands and capacities. A response surface methodology is used to evaluate the annual exceedance probability in each specified, damage-related performance level. The nonlinear dynamic response of the structure to the seismic action is studied considering, among the uncertainties, the peak acceleration and the frequency content of the ground motion. Advantages and disadvantages of two types of response surface formulations are discussed, including two ways of considering dispersion produced by different ground motions. The procedure is illustrated with two examples: an elevated water tank and a five-story portal frame. Finally, the method is applied to the calibration of a deterministic, factored design code approach with multiple performance objectives.
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48

TODOROKI, Akira, and Atsushi Iwasaki. "3405 Optimal Design with Response Surface using Microsoft Excel." Proceedings of Design & Systems Conference 2001.11 (2001): 344–47. http://dx.doi.org/10.1299/jsmedsd.2001.11.344.

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49

Krajnik, P., J. Kopac, and A. Sluga. "Design of grinding factors based on response surface methodology." Journal of Materials Processing Technology 162-163 (May 2005): 629–36. http://dx.doi.org/10.1016/j.jmatprotec.2005.02.187.

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50

Song, Guo-hui, Yu Wu, and Cong-xin Li. "Engineering design optimization based on intelligent response surface methodology." Journal of Shanghai Jiaotong University (Science) 13, no. 3 (June 2008): 285–90. http://dx.doi.org/10.1007/s12204-008-0285-3.

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