基于改进粒子群算法的复杂曲面轮廓度误差评定及可视化

Profile Error Evaluation of the Surface Based on Improved Particle Swarm Optimization and Its Visualization

针对复杂曲面轮廓度误差的求解是一个复杂的非线性寻优问题，将改进的粒子群算法与细分曲面逐次逼近的方法相结合，实现了复杂曲面轮廓度误差值的精确计算和评定结果可视化。利用双3次B样条曲面进行理论廓面的拟合，从最小条件准则出发，建立了曲面轮廓度误差的数学模型；通过细分曲面逐次逼近的方法，计算出点到曲面的最小距离。在对基本粒子群算法分析的基础上，引入了非线性动态惯性权重系数和杂交算子，提高了算法的精度和效率。以VRML作为三维展示平台、Java Applet作为控制核心，实现了面轮廓度误差评定的可视化、网络化。

As to the complex surface profile error evaluation is an involved nonlinear optimization problem, using improved particle swarm optimization algorithm combined with the method of subdivision surface and successive approximation,the accuracy calculation of complex surface profile error and the visualization of evaluation result are realized. After the theoretical surface is fitted by double cubic B-spline surface, the mathematical model of surface profile error is created under the minimum condition rule. With the help of subdivision surface and successive approximation, the minimum distance between measuring points and surface is obtained. Based on the analysis of basic particle swarm optimization algorithm, nonlinear dynamic inertial weight factor and hybrid operator are introduced to improve the efficiency and accuracy. Taking VRML as the 3D displaying platform and Java Applet as the controlling core,the surface profile error evaluation is visualized and networked.