Abstract
The present paper discusses response surface methodology (RSM) as an efficient system for building a mathematical model and optimization of the process of polyurethane coating on acrylonitrile butadiene styrene using a single axis robotic applicator. In this work, RSM is used to optimize the process inputs such as part—applicator distance, paint flow rate, and paint viscosity to obtain the optimal values of process outputs; dry film thickness (DFT), rating value (R), and distinctness of image (DOI). The optimization was done to achieve high values of R and DOI with minimal DFT to reduce overall process cost. A 23 full factorial central composite design experimental design was employed. ANOVA showed satisfactory levels of coefficient of determination values (R 2) for all three responses. The optimized results were validated experimentally. Under optimal value of process parameters high rating value (7.76) and high distinctness of image (93.137) was achieved with low dry film thickness of 29.99 μm.
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References
Blank WJ (1992) Novel polyurethane polyols for waterborne and high solids coatings. Prog Org Coat 20(3–4):235–259
Montgomery DC (2001) Design and analysis of experiments, 5th edn. Wiley, New York
Paul S (2002) Painting of plastics: new challenges and possibilities. Surf Coating Int B Coating Trans 85(2):79–86
Im Kyoung-Su, Lai M-C, Yoon Suck-Ju (2003) Spray characteristics on the electrostatic rotating bell applicator. J Mech Sci Technol 17(12):2053–2065
David CC, Prasad NA, Alfred AR (2003) Experimental verification of deposition models for automotive painting with electrostatic rotating bell atomizers. Springer Tr Adv Robot 5(2):136–145
Ye Q, Domnick J, Scheibe A, Pulli K (2005) Numerical simulation of electrostatic spray-painting processes in the automotive industry. Springer Verlag Berlin, Heidelberg, pp 261–275, High-Performance Computing in Science and Engineering'04
Myers RH (1971) Response surface methodology. Allyn and Bacon, New York
Lipson C, Sheth NJ (1973) Statistical design and analysis of engineering experiments, 1st edn. Mc-Graw Hill, USA, January 1973
Gunaraj V, Murugan N (1999) Application of response surface methodologies for predicting weld base quality in submerged arc welding of pipes. J Mater Process Tech 88(1–3):266–275
Box GEP, Hunter JS (1957) Multi-factor experimental design for exploring response surfaces. Ann Math Stat, 28th ed, pp 195–241
Box GEP, Hunter JS (1961) The fractional factorial designs, parts I and II. J Technometry 3(4):311–458
Guthrie JT, Weakley AP (1999) The influence of electrostatic paint application process variables on the orange-peel effect or: a case study in the use of experimental design methods. Surf Coating Int B Coating Trans 82(8):379–383
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Arunkumar, N., Venkatesh, P., Srinivas, K.S. et al. Response surface modeling and optimization of single axis automatic application of automotive polyurethane coatings on plastic components. Int J Adv Manuf Technol 63, 1065–1072 (2012). https://doi.org/10.1007/s00170-012-3970-1
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DOI: https://doi.org/10.1007/s00170-012-3970-1