Response Surface Methodology in Application of Optimal Manufacturing Process of Axial-Flow Fans Adopted by Modern Industries
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 6, November 2018, Pages: 235-241
Received: Oct. 2, 2018;
Accepted: Oct. 19, 2018;
Published: Nov. 6, 2018
Views 664 Downloads 73
Cheruiyot Chepkeitany Joseph, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Waititu Anthony, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Wanjoya Anthony, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Response surface methodology (RSM) is a collection of mathematical and statistical techniques that help in model building and analysis of problems in which a response (output variable) of interest is influenced by numerous factors (independent variables) with the objective of optimizing this response. It is widely used in many disciplines such as Manufacturing Industries, Engineering and Agricultural Sciences. Different types of axial flow fans are being used in manufacturing industries in cooling mechanisms where a lot of heat is produce by the machines and in semi-arid and arid areas to regulate room temperatures. Though little research has been done to ascertain the strength of axial flow fans, there was need to study the optimal specifications of fans to be manufactured by industries to produce a more efficient, strong and long lasting cooling fan. This new focus from the manufacturers represents new quality fans that significantly increase market profitability. In this study, second order response surface model was used to estimate the axial-flow fan parameters. Three experimental factors or specifications were evaluated, that is; the hole type in the fan "spyder" (blades), the barrel surface type onto which the “spyder” was placed, and the assembly method type for the two components. Central composite designs satisfying all the rotatability conditions were constructed. The D- and A- optimal criteria were used to evaluate the effectiveness of the design. Secondary data was used to obtained second order optimal model for manufacturing process of axial-flow fans adopted by industries. The partial derivatives of the model were used to determine the stationary points of the response surface. Contour plots were used to determine whether the stationary were at maximum, minimum or saddle points. R statistical program was used in analysis of the data.
Cheruiyot Chepkeitany Joseph,
Response Surface Methodology in Application of Optimal Manufacturing Process of Axial-Flow Fans Adopted by Modern Industries, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 6,
2018, pp. 235-241.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
U. S. Department of Energy (1989). Improving Fan System Performance: a sourcebook for industry.
Montgomery, D. C. (2001). Design and Analysis of Experiments 5th edition. New York: John Wiley & Sons.
Box, G. E., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society Ser. B, 13,, pp 195-241.
Myers, R. H. (1976). Response Surfaces Methodology. Boston: Allyn and Bacon.
Demirel, M., & Kayan, B. (2012). Application of response surface methodology and central composite design for the optimization of textile dye degradation by wet air oxidation. Int. J. Ind. Chem. 3, 1-10.
Azami, M., Bahram, M., Nouri, S., & Naseri, A. (2012, 2013). Central composite design for the optimization of removal of the azo dye, Methyl Red, from waste water using Fenton reaction. Curr. Chem. Lett. 2, 57-68.
Polowczyk, I., & Kozlecki, T. (2017). Central composite design application in oil agglomeration of talc. Physicochem. Probl. Miner. Process. 53(1), 1061-1078.
Box, G. E., & Hunter, J. S. (1957). Multifactor Experimental Designs for Exploring Response Surfaces. Annals of Mathematical Statistics 28, pp. 195-241.
Oehlert, G. W. (2000). Design and analysis of experiments. New York: W. H. Freeman and Company.
Myers, R. H., & Montgomery, D. C. (2002). Response Surface Methodology 2nd edition. New York: John Wiley & Sons.
Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd Edition. New York: Wiley and Sons Inc.
Anderson-Cook, C. M., Borror, C. M., & Montgomery, D. C. (2009). Response Surface Design Evaluation and Comparisons. Journal of Statistical Planning and Inference, 139, 629-641.
Kalavathy, M. H., Regupathi, I., Pillai, M. G., & Miranda, L. R. (2009). Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM). Colloids Surf B, 7035–45.
Haber A, Runyun RP. General statistics. 3rd ed. Reading, MA: Addision-Wesley; 1977.
Kim, H. K., Kim, J. G., Cho, J. D., & Hong, J. W. (2003). Optimization and characterization of UV-curable adhesives for optical communications by response surface methodology. Polym Test, 22: 899–906.
O’brien, R. M. (2007). A Caution Regarding Rules of Thumb. Quality & Quantity.
K. Vimalashanmugam, & T. Viruthagiri, (2012). Response Surface Methodology Optimization Of Process Parameters for Xylanase Production by Aspergillus fumigatus in SSF using Central Composite Design. International Journal of Engineering Research and Applications, pp.277-287.