大地算法:优化机身装配过程中临时紧固件布置的新方法

S. Lupuleac, Tatiana Pogarskaia
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引用次数: 0

摘要

设计/方法/途径组合性质、不确定的输入数据、对机械性能和几何公差的敏感性是紧固件优化问题的具体特征。由于目标函数的计算需要多次解决接触问题,这些特点使得用标准方法解决问题非常耗费资源。该研究对大地算法(GA)进行了扩展描述,这是一种避免多重目标函数计算的新型非迭代优化方法。优化的目标函数是已安装紧固件的数量和连接部件之间的接触质量。该算法已应用于空中客车公司装配过程的优化。原创性/价值在保持结果质量的前提下,GA 的性能比标准优化算法高出几个数量级。由于无法自动应用于其他领域的优化问题,因此装配过程的特殊性是 GA 的主要局限性。由于工作速度快、结果质量高,因此可以将其用于解决商用客机生产装配线上的实际优化问题。
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Geodesic algorithm: new approach to optimization of temporary fastener arrangement in airframe assembly process
Purpose The purpose of the current study is development of effective and fast algorithm for optimization the arrangement of temporary fasteners during aircraft assembly. Design/methodology/approach Combinatorial nature, uncertain input data, sensitivity to mechanical properties and geometric tolerances are the specific features of the fastening optimization problem. These characteristics make the problem-solving by standard methods very resource-intensive because the calculation of the objective function requires multiple solution of contact problems. The work provides an extended description of the geodesic algorithm (GA) which is a novel non-iterative optimization approach avoiding multiple objective function calculations. Findings The GA makes it possible to optimize the arrangement of temporary fasteners during the different stages of the assembly process. The objective functions for the optimization are number of installed fasteners and quality of contact between joined parts. The mentioned properties of the GA also make it possible to introduce an automatic procedure for optimizing fastener arrangement into everyday practice of aircraft manufacturing. Practical implications The algorithm has been applied to optimization of the assembly process in Airbus company. Originality/value Performance of the GA is orders of magnitude greater than standard optimization algorithms while maintaining the quality of results. The use of the assembly process specifics is the main limitation of the GA, because it cannot be automatically applied to optimization problems in other areas. High speed of work and quality of the results make it possible to use it for real optimization problems on assembly line in the production of commercial airliners.
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