2019
DOI: 10.1007/s12206-019-0828-6
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Design optimization of magnetorheological damper geometry using response surface method for achieving maximum yield stress

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Cited by 21 publications
(9 citation statements)
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References 28 publications
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“…Parameters such as flange length, gap width, piston head housing thickness, the radius of piston core, and gap length are often optimized using software before being fabricated. Parlak et al [45] used ANSYS, a finite element software, while Singh et al [46] and Nanthakumar et al [47] used an optimization algorithm to optimize the geometrical parameters of the damper. Basically, in terms of the design, modifications are done to increase the generated magnetic flux density with a given value of applied current.…”
Section: Related Workmentioning
confidence: 99%
“…Parameters such as flange length, gap width, piston head housing thickness, the radius of piston core, and gap length are often optimized using software before being fabricated. Parlak et al [45] used ANSYS, a finite element software, while Singh et al [46] and Nanthakumar et al [47] used an optimization algorithm to optimize the geometrical parameters of the damper. Basically, in terms of the design, modifications are done to increase the generated magnetic flux density with a given value of applied current.…”
Section: Related Workmentioning
confidence: 99%
“…Kumar et al 5 optimized the geometry of MR damper to obtain the maximum magnetic flux density in the flow damping gap. Nanthakumar et al 6 studied the influence of design variables on the yield stress output response and the interaction relationship between the design variables, and obtained the geometric design variables of the magnetic circuit in the MR damper. Through the full factorial research experimental design, the quadratic expression of the response variable was established and optimized.…”
Section: Introductionmentioning
confidence: 99%
“…This technique can be used to determine the relationship between various design parameters and the desired responses with the advantages of design cycle-shortening and experimental cost-saving. 2224 Some scholars have tried to apply this technique to the fields relevant to the optimization of spray coatings. 25,26 For example, Müller and Kleinebudde 27 especially conducted a complete 3 2 factorial design of experiment (DOE) to correlate the atomization and pattern air pressure with the spray width using RSM.…”
Section: Introductionmentioning
confidence: 99%