The additively repaired blade requires intelligent grinding process to restore the blade profile, and the essential prerequisite is to reconstruct a reliable reference surface at the repaired area with local deformation. In this paper, a novel surface reconstruction framework based on the optimal reference iteration in the parameter direction is developed to overcome this challenging problem through three steps. In the framework, a parameter alignment algorithm based on the sorting features is proposed to align the fitted blade cross-section curves at first by considering the local deformation and curvature variation, while avoiding the alignment falling into local optimum. Then a curve discretization method based on the curvature features is presented to discretize the multi-section curves for preserving the high curvature variation features to the most extent. Based on these two steps, a curve fitting strategy based on the -directional optimal reference iteration is suggested for surface reconstruction by virtue of the optimal reference principle and VMM (Variance-Minimization Matching) algorithm. Both the simulation and experimental results demonstrate the effectiveness of the proposed framework from the perspectives of the blade position errors and the profile accuracy after robotic grinding. The average point cloud error between the reconstructed model and the standard model is 0.018 mm, which is decreased by 51.7 % compared with the state-of-the-art method.

中文翻译：

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基于最佳参考迭代的表面重建框架，用于局部变形增材修复叶片的机器人磨削

增材修复叶片需要智能磨削工艺来恢复叶片轮廓，其必要前提是在局部变形的修复区域重建可靠的参考面。本文开发了一种基于参数方向最优参考迭代的新型表面重建框架，通过三个步骤克服了这一具有挑战性的问题。在该框架中，提出了一种基于排序特征的参数对齐算法，考虑局部变形和曲率变化，首先对拟合的叶片截面曲线进行对齐，同时避免对齐陷入局部最优。然后提出一种基于曲率特征的曲线离散化方法，对多段曲线进行离散化，最大限度地保留高曲率变化特征。基于这两个步骤，利用最优参考原理和VMM（方差最小化匹配）算法，提出了一种基于负向最优参考迭代的曲线拟合策略来进行曲面重建。仿真和实验结果从刀片位置误差和机器人磨削后的轮廓精度的角度证明了所提出框架的有效性。重建模型与标准模型之间的平均点云误差为0.018 mm，与state-of-the-art方法相比减少了51.7%。