In this paper, an optimized error sensitivity analysis (ESA) approach based on stream-of-variation (SOV) theory in multi-axis precise motion platform (MPMP) is introduced. Unlike the conventional ESA method, this approach utilizes stream-of-variation theory to establish error model firstly. By regarding each axis as a station, the error propagation and deviation accumulation processes are clear station by station. Through obtaining the deviations after each station, the sensitive stations can be developed, and corresponding error terms are collected. Thus, by analyzing the appearance frequency of the errors, a precise sensitive order can be developed, and a sensitivity results with different steps is given. Through ignoring the insensitive and irrelevant errors, the computational volume of error model can be saved, and the efficiency can be improved. A case study about a typical MPMP used in laser welding system (LWS) is carried out, and the results indicate that the optimized ESA approach is more efficient and accurate, and it is flexible as well. This ESA approach is not only accurate in developing sensitive levels, but also balancing the systematic accuracy and working efficiency, which is helpful to provide informative guide to engineers.

本文介绍了一种基于变异流（SOV）理论的多轴精密运动平台（MPMP）中的优化误差敏感性分析（ESA）方法。与传统的ESA方法不同，该方法首先利用变异流理论建立误差模型。通过将每个轴视为一个站，可以逐站清除错误传播和偏差累积过程。通过获得每个站点之后的偏差，可以开发敏感站点，并收集相应的误差项。因此，通过分析错误的出现频率，可以建立精确的敏感度顺序，并给出不同步骤的敏感度结果。通过忽略不敏感和无关的错误，可以节省错误模型的计算量，并且可以提高效率。以激光焊接系统（LWS）中典型的MPMP为例进行了案例研究，结果表明，优化的ESA方法更加有效，准确，并且灵活。这种ESA方法不仅在开发敏感级别方面是准确的，而且在系统准确性和工作效率之间取得了平衡，这有助于为工程师提供指导。