A coordination modelling approach for assembly of multi-constrained objects based on measured skin model

Abstract

Purpose

The purpose of this paper is to propose an assembly coordination modelling approach based on measured data for assembly quality control of multi-constrained objects in aircraft assembly. This approach aims to establish a high-precision digital mirror of physical assembly system in the virtual environment, with which the assembly process in the virtual environment can be performed synchronously with that in the physical world.

Design/methodology/approach

This paper presents a realistic geometrical representation model based on measured point cloud, as well as the multiple constraints modelling methods for local and global constraints with the proposed representation model. For the assembly target optimization, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, where the particle swarm optimization and simulated annealing algorithm are combined to calculate the optimal solutions.

Findings

As shown in the validation results, the minimum easiness value for easiness model in global optimization is 3.01, while the best value for weighting model by adjusting weights for more than 10 times is 1.94. The results verify that the proposed coordination modelling approach is effective to the assembly of multi-constrained objects, and the optimization model has an obvious advantage over the traditional weighting method.

Originality/value

This paper provides a new idea for the fine control of assembly quality of non-ideal components by introducing the measured data into the on-line assembly process. Besides, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, which overcomes the problem of traditional weighting model wherein the weightings are difficult to determine.

Citation

Jiang, Y., Huang, X., Li, S. and Deng, Z. (2019), "A coordination modelling approach for assembly of multi-constrained objects based on measured skin model", Assembly Automation, Vol. 39 No. 2, pp. 380-391. https://doi.org/10.1108/AA-04-2018-058