A difference measurement points planning method for large-scale surface of aircraft
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摘要:
数字化测量技术在飞机大尺寸零部件检测的应用日益广泛，合理规划测量点数量和分布以精确地描述待测特征已成为关键问题之一。针对复杂曲线曲面的测量点规划问题，提出了一种基于确定性表达的测量点差异性规划方法。利用非均匀有理B样条（NURBS）理论精确拟合自由曲线，通过粒子群优化算法综合优化控制点及权因子，构建高精度的拟合曲线。提出了面向曲率特性和测量不确定度的布点策略，结合曲面特性建立完整、高效的测量点规划流程。基于CAA模块程序化实现了测量点规划方法，并以试验件为验证对象，验证了所提方法的可行性和系统的有效性。
Abstract:Digital measurement technology has been gradually applied in aircraft large-scale component measurement, which also brings about many issues, and the fundamental one is how to plan the number and layout of measurement points to well describe the curve or surface. Aimed at solving the issue of complicated curve or surface measurement points planning, a novel difference measurement points planning method based on deterministic representation is proposed. The curve's deterministic representation is acquired by using Non-Uniform Rational B-Splines (NURBS) theory, and the particle swarm optimization is adopted to optimize the control points and corresponding weights factors to construct the high-precision fitting curve. Two different measurement point planning strategies considering curvature and measurement uncertainty are designed, and combined with surface characteristics, a complete and high efficiency measurement point planning process is constructed. The measurement points planning algorithm proposed in this paper is programmed based on the CAA, and by taking test piece as experiment object, the feasibility of the algorithm and the effectiveness of the system are verified.
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图 1 试验件测量不确定度及曲率分布
Figure 1. Measurement uncertainty and curvature distribution of test piece
图 6 曲线轮廓度公差带及测量不确定度允差
Figure 6. Tolerance zone and measurement uncertainty tolerance of curve
图 7 曲面轮廓度公差带及测量不确定度允差
Figure 7. Tolerance zone and measurement uncertainty tolerance of surface
图 10 试验件局部曲面片布点过程
Figure 10. Points distribution process of partial surface of test piece
图 11 不同布点密度布点结果对比
Figure 11. Comparison of points distribution under different measurement points density
表 1 粒子群优化算法参数
Table 1. Parameters of particle swarm optimization algorithm
参数 数值 c1、c2 2.05 vmax、vmin 0.01 w 0.9 Np 200 
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表 2 不同布点密度下测量结果对比
Table 2. Comparison of measurement result under different measurement points density
布点密度 布点数量 测量时长/min 重构孔洞数量 拟合精度/mm 20×20 8 868 98 21 0.037 3 本文密度 9 986 110 5 0.019 4 10×10 19 958 220 4 0.015 6
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