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Advanced Mathematical Modelling for Glass Surface Optimization with PSO Becton Dickinson India Technical Center

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
Pandey, Pawan, author.
Contributor:
Askari, Hasan
Raadhaasaminathan, Sreebalajinarayanan
Conference Name:
International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility (2019-10-11 : Chennai, India)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2019
Summary:
In automotive door engineering, fitting the side door glass surface from styling into the cylinder or torus is the basic requirement. Optimization is required to do this, which requires a solver which could be efficacious for best surface fitting. This paper propounds a methodology which could be used for fitting a side door glass surface from styling into the cylinder or torus. The method will significantly help in developing the required surface and can successfully eliminate the cumbersome manual calibrations. The mathematical model mentioned is a novel approach based on "Particle Swarm Optimization" ("PSO" will be used to represent in the paper) towards surface optimization technique. VB script is used to make it applicable in CATIA but could be easily applied in any other programming language like python, java et ceteraUsually the surface fitting problems deals with the initial guess of the required surface and then its further optimization. Herewith we have discussed some geometrical methods to find the initial guess of the cylindrical and toroid surface and then Particle Swarm Optimization for refining of the obtained data. This article aims at providing the best surface fit at the initial stage itself so that the faster output convergence rates are achieved. The proposed algorithm is efficient and easy to code, and the experiment results indicate its effectiveness. Since we have avoided analytical method and used an evolutionary stochastic approach in the process in which the chances of getting stuck in local minimum are very limited
Notes:
Vendor supplied data
Publisher Number:
2019-28-0104
Access Restriction:
Restricted for use by site license

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