Operations in assembling and joining large size aircraft components are changed to novel digital and flexible ways by digital measurement assisted alignment.Positions and orientations(P&O)of aligned components are critical characters which assure geometrical positions and relationships of those components.Therefore,evaluating the P&O of a component is considered necessary and critical for ensuring accuracy in aircraft assembly.Uncertainty of position and orientation(U-P&O),as a part of the evaluating result of P&O,needs to be given for ensuring the integrity and credibility of the result;furthermore,U-P&O is necessary for error tracing and quality evaluating of measurement assisted aircraft assembly.However,current research mainly focuses on the process integration of measurement with assembly,and usually ignores the uncertainty of measured result and its influence on quality evaluation.This paper focuses on the expression,analysis,and application of U-P&O in measurement assisted alignment.The geometrical and algebraical connotations of U-P&O are presented.Then,an analytical algorithm for evaluating the multi-dimensional U-P&O is given,and the effect factors and characteristics of U-P&O are discussed.Finally,U-P&O is used to evaluate alignment in aircraft assembly for quality evaluating and improving.Cases are introduced with the methodology.
异常识别是多元统计过程控制(MSPC,Multivariate Statistical Process Con-trol)方法有效应用的关键.针对现有研究对历史异常信息利用的不足,综合考虑了主成分变量贡献率与重构误差变量贡献率对异常识别的影响,将两种变量贡献率进行归一化处理并求和得到综合变量贡献率;提出了一种基于综合变量贡献率的MSPC异常识别方法,并基于matlab计算平台实现了该算法.通过田纳西过程故障模式仿真及异常识别,对该方法的应用及算法有效性进行了实例验证.