Optimal design of clamping sequence in sheet metal assembly based on genetic algorithm

Jie Wang; Jinze Zhao; Yuan Gao; Tingyu Zhang; Yahui Wang

doi:10.1117/12.3047640

27 September 2024 Optimal design of clamping sequence in sheet metal assembly based on genetic algorithm

Jie Wang, Jinze Zhao, Yuan Gao, Tingyu Zhang, Yahui Wang

Author Affiliations +

Proceedings Volume 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024); 132610P (2024) https://doi.org/10.1117/12.3047640
Event: 10th International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 2024, Wuhan, China

Abstract

The sequence in which the clamps are applied has a significant influence on the location error and dimensional variability for non-rigid parts assembly. This paper presents a novel clamping sequence optimization methodology applicable for deformed parts assembly with the main objective of minimizing the part-to-part gap. The methodology combines finite element modelling and genetic algorithm to realize the generation of an optimal clamping sequence for compliant parts. Finite element models are developed to simulate the transformation of geometry under the constraints of clamps and the computation of part-to-part gaps is achieved by updating the geometry along the clamping sequences. A genetic algorithm is applied to automatically search for the optimal clamping sequence. In the genetic algorithm, a coarse mesh is used to solve the time-consuming problem and a fine mesh is used to test the results. The methodology is demonstrated and validated using the results of a case study, which obtains an accurate part-to-part gap with a 90% reduction in computation time to identify the optimal clamping sequence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Wang, Jinze Zhao, Yuan Gao, Tingyu Zhang, and Yahui Wang "Optimal design of clamping sequence in sheet metal assembly based on genetic algorithm", Proc. SPIE 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 132610P (27 September 2024); https://doi.org/10.1117/12.3047640

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