Adaptive allocation of the machining allowance is the crucial factor in ensuring the machining accuracy of complex parts. In this work, we present a multi-objective constraint registration method. First, an improved point cloud segmentation method is developed by combining point search and region data expansion algorithms. Afterward, the machining allowance is accurately calculated by using statistical analysis and multi-point sampling strategies to enhance the calculation accuracy of the point-to-triangular patch distance. Finally, a registration objective function is established by considering the allowance constraints of various geometric regions of the workpiece, and the particle swarm optimization algorithm is used to solve the optimum solution. The proposed multi-constraint registration method realizes optimal allocation of the allowance in different regions, which offers a reference coordinate system for the robotic milling of complex free-formed parts. Simulation and experimental results reveal that the developed method satisfies the minimum registration error while ensuring the allocation of allowance in the robotic milling of the casing cavity compared with other methods.

中文翻译：

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一种考虑复杂零件机器人铣削的多余量约束的点云配准算法


加工余量的自适应分配是确保复杂零件加工精度的关键因素。在这项工作中，我们提出了一种多目标约束注册方法。首先，通过结合点搜索和区域数据扩展算法，开发了一种改进的点云分割方法;之后，通过使用统计分析和多点采样策略准确计算加工余量，以提高点到三角形补丁距离的计算精度。最后，考虑工件各几何区域的余量约束，建立配准目标函数，并采用粒子群优化算法求解最优解。所提出的多约束配准方法实现了不同区域余量的最优分配，为复杂自由成型零件的机器人铣削提供了参考坐标系。仿真和实验结果表明，与其他方法相比，所开发的方法在保证套管腔机器人铣削余量的同时满足了最小的对准误差。