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Advanced Mathematical Modelling for Glass Surface Optimization with PSO
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
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 etc.
Usually 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.
CitationPandey, P., Askari, H., and Raadhaasaminathan, S., "Advanced Mathematical Modelling for Glass Surface Optimization with PSO," SAE Technical Paper 2019-28-0104, 2019, https://doi.org/10.4271/2019-28-0104.
Data Sets - Support Documents
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