基于变尺度混沌算法的曲面品质优化

Surface quality optimization is a common problem in surface reconstruction. In the design of high-end products such as aerospace and automobile, if the reconstructed surfaces are required to have high-order continuity, a lot of optimization work is often needed. In order to obtain smooth and high-quality surfaces conveniently, an optimization method of surface quality based on a mutative scale chaos algorithm is proposed. Adjustable parameters are introduced. The target surface can be deformed by flexibly adjusting a number of parameters under the G¹ continuity constraint between neighboring NURBS patches. A mathematical model of mutative scale chaos optimization is established, and the optimal solution of the adjustable parameters is calculated to obtain a high-quality surface with the smallest deformation compared with the original surface. The robustness and practicability of this method are verified by case analysis. The isolux analysis of the optimized surface is carried out. The outcomes demonstrate that the mutative scale chaotic algorithm-based surface quality optimization technique may guarantee the surface's quality and enhance the effectiveness of surface reconstruction.