Optimization of transfer station parameters of a laser tracker based on the particle swarm algorithm for a large part experiencing 3D deformation

LIU Huasen; CHEN Ken; WANG Guolei

doi:10.16511/j.cnki.qhdxxb.2020.25.045

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Journal of Tsinghua University(Science and Technology) ›› 2021, Vol. 61 ›› Issue (9) : 979-985. DOI: 10.16511/j.cnki.qhdxxb.2020.25.045

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Optimization of transfer station parameters of a laser tracker based on the particle swarm algorithm for a large part experiencing 3D deformation

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Abstract

The shapes of large aviation workpieces can deform due to temperature changes, which significantly impacts precise digital measurements of the product shape and the accuracy of component docking. This paper describes a method to optimize the transfer station parameters of a laser tracker to account for three-dimensional deformation of the workpiece. The center of a three-dimensional model of the workpiece was calculated using the ANSYS finite element thermal analysis to account for the thermal expansion of the workpiece. An objective function was then defined for optimizing the transfer station parameters that considered the three-dimensional thermal expansion of the workpiece. Numerous individuals in a population of models were randomly initialized to calculate the adaptive values for each individual. The particle swarm optimization (PSO) algorithm was then used to iteratively optimize the individuals in the population to obtain the optimal tracking transfer parameters. This method takes into account the three-dimensional thermal deformation of large workpieces caused by temperature changes to improve the tracker measurement accuracy.

Key words

three-dimensional thermal expansion / tracking device transformation / parameter optimization / particle swarm optimization (PSO)

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LIU Huasen, CHEN Ken, WANG Guolei. Optimization of transfer station parameters of a laser tracker based on the particle swarm algorithm for a large part experiencing 3D deformation[J]. Journal of Tsinghua University(Science and Technology). 2021, 61(9): 979-985 https://doi.org/10.16511/j.cnki.qhdxxb.2020.25.045

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