Despite the growing use of machine tools for in-process measurement, the uncertainty evaluation of on-machine probing has mostly remained limited to the method specifically developed in ISO 15530-3 for coordinate-measuring machines. These methods reduce the on-machine measurement problem to a single-output system, so that the law of propagation of uncertainty becomes applicable, which excludes any covariance effect between the input quantities. This study proposes a methodology that inclusively estimates the uncertainty associated with any probing within the working space of a five-axis machine tool. Defined by the machine's forward kinematic model, the on-machine measurement function receives the machine geometric errors and the axis positions for a probed point set, and estimates its compensated position in the workpiece frame. The proposed uncertainty estimator assembles the covariance matrices associated with these input quantities and evaluates the measurement uncertainty through an adaptive Monte Carlo method. Unlike the task-specific method given by ISO 15530-3, this scheme eliminates the need for any part's calibrated counterpart and involves the covariance between the input quantities. The experimental verification of the new method includes the on-machine measurement of the length of a gauge block and the diameter and sphericity of a precision sphere through highly diverse axis positions of a five-axis machine tool. Over the 225 possible combinations of 15 point sets (each of size 2) probed on the gauge block, the coverage probability of the expanded uncertainty (for a coverage factor of 2) estimated for the gauge's length is 90%. Then, 10 point sets (each of size 25) collected on the sphere create 10 accumulated pools, and from each, 200 randomly drawn samples estimate the sphere's diameter. The coverage probabilities of the expanded uncertainty estimated for the pools built of up to 7 point sets remain above 94%. These levels of confidence are comparable to the theoretical level (95%).

尽管越来越多地使用机床进行在线测量，但机上探测的不确定性评估大多仍局限于 ISO 15530-3 中专门为坐标测量机开发的方法。这些方法将机上测量问题简化为单输出系统，因此不确定性传播定律变得适用，排除了输入量之间的任何协方差效应。这项研究提出了一种方法，可以全面估计与五轴机床工作空间内的任何探测相关的不确定性。由机器的正向运动模型定义，在机测量功能接收机器几何误差和探测点集的轴位置，并估计其在工件框架中的补偿位置。建议的不确定性估计器组装与这些输入量相关联的协方差矩阵，并通过自适应蒙特卡罗方法评估测量不确定性。与 ISO 15530-3 给出的特定任务方法不同，该方案不需要任何零件的校准对应物，并涉及输入量之间的协方差。新方法的实验验证包括通过五轴机床的高度多样化的轴位置对量块的长度以及精密球体的直径和球度进行机上测量。在量块上探测的 15 个点集（每个大小为 2）的 225 种可能组合中，针对量块长度估计的扩展不确定性（覆盖因子为 2）的覆盖概率为 90%。然后，在球体上收集的 10 个点集（每个大小为 25）创建了 10 个累积池，并且从每个点中随机抽取 200 个样本来估计球体的直径。为最多 7 个点集构建的池估计的扩展不确定性的覆盖概率保持在 94% 以上。这些置信水平与理论水平 (95%) 相当。