面向復(fù)雜曲面加工的NURBS曲線逼近及插補算法研究
[Abstract]:Complex surface parts are usually composed of free-form curves and surfaces, in which the approximation and interpolation algorithm of NURBS curves is the key technology in digital manufacturing of complex surfaces. In this paper, the NURBS interpolation and approximation theory involved in the machining of complex surfaces are deeply studied. A NURBS curve approximation algorithm based on feature point extraction and improved particle swarm optimization (PSO) is proposed, which compresses the number of control vertices of NURBS approximation curve in profile reconstruction of complex surfaces. In order to improve the approximation accuracy of double NURBS tool path generated by the same node vector in the machining of complex surface, an optimization model of tool pivot point curve approximation with variable weight is constructed, and the improved coevolutionary genetic algorithm is used to solve the model. The optimal weight value of the curve is obtained. Considering the shortcomings of traditional NURIBS interpolation algorithm in interpolation precision and interpolation velocity volatility, an interpolation algorithm based on improved S-type velocity programming and Steffens type parameter calculation is proposed. Taking the impeller blade as an example, the validity of the algorithm is verified by comparing the algorithm. The main contents of this paper are as follows: in the first chapter, the research status of free curve approximation and interpolation algorithm in complex surface machining is summarized, and the defects and solutions of the present methods are introduced. This paper expounds the research significance of this paper, and introduces the organization structure of this paper. In chapter 2, a NURBS curve approximation algorithm for compressed control vertices is proposed. The curvature of discrete points is calculated by means of equal chord length method. The characteristic points of discrete point sequence are extracted based on curvature analysis and the initial approximation curve is constructed. Based on the error control, the interpolation point is added and the approximation curve is updated. The improved particle swarm optimization algorithm is used to optimize the position of the control vertex, and the final approximation curve is obtained. In chapter 3, a double NURBS tool path generation algorithm with variable weights is proposed. Based on the error control, some discrete data of tool center point and tool pivot point are selected, and the initial double NURBS curve is constructed by using the same node vector, and the tool pivot point curve approximation optimization model with variable weight is constructed. The improved coevolutionary genetic algorithm is used to adjust the weight value of the tool pivot point curve, and the approximation error of the curve is reduced. In chapter 4, an improved S-type velocity programming method and a Steffens-type interpolation parameter calculation method with parameters are presented. The curve segment information is obtained by adaptive interpolation, and the maximum acceleration is adjusted adaptively according to the curvature information and the speed is precisely controlled. The traditional S-type speed planning algorithm is improved. The forward and inverse interpolation is used to accurately determine the deceleration point, and the Steffensen method with parameters is used to calculate the curve interpolation parameters. The derivation operation is avoided, the real-time performance of the interpolation is enhanced, and the velocity fluctuation is effectively controlled. The fifth chapter takes the impeller blade as an example to verify the theory and algorithm proposed in this paper. The experimental results show that the NURBS approximation curve generated by this algorithm has fewer control vertices and higher approximation accuracy than the traditional method. The improved NURBS interpolation algorithm can effectively reduce the chord height error and control the velocity volatility. In the sixth chapter, the main research contents are summarized, and the future research work is prospected.
【學(xué)位授予單位】:浙江大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:TG659
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