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Response Surface Algorithms for Engine Mount Optimization in Motorcycles

[+] Author Affiliations
Sudhir Kaul

University of Pretoria, Pretoria, South Africa

Anoop K. Dhingra

University of Wisconsin - Milwaukee, Milwaukee, WI

Paper No. DETC2009-86407, pp. 711-726; 16 pages
doi:10.1115/DETC2009-86407
From:
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 35th Design Automation Conference, Parts A and B
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4902-6 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME

abstract

This paper presents a Response Surface Modeling (RSM) approach for solving the engine mount optimization problem for a motorcycle application. A theoretical model that captures the structural dynamics of a motorcycle engine mount system is first used to build the response surface model. The response surface model is then used to solve the engine mount optimization problem for enhanced vibration isolation. Design of Experiments (DOE), full factorial and fractional factorial formulations, are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the variables and the significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position vectors and orientation vectors. It is seen that RSM leads to a substantial reduction in computational effort and yields a simplified input-output relationship between the variables of interest. However, as the number of design variables increases and as the response becomes irregular, conventional use of RSM is not viable. Two algorithms are proposed in this paper to overcome the issues associated with the size of the governing experiments and problems associated with modeling of the orientation variables. The proposed algorithms divide the design space into sub-regions in order to manage the size of the governing experiments without significant confounding of variables. An iterative procedure is used to overcome high response irregularity in the design space, particularly due to orientation variables.

Copyright © 2009 by ASME

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