Manufacturing Technology 2023, 23(4):557-563 | DOI: 10.21062/mft.2023.056

Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm

Wei Zhang ORCID...
School of intelligent manufacturing, Xiamen City University, Xiamen, Fujian 361008, China

Traditional assembly sequence solving methods often face problems such as combinatorial explosion and low efficiency in solving complex products with multiple parts. To improve the level of assembly sequence planning (ASP), an interference matrix is established to express the basic assembly information of a product. Taking the stability of the assembly sequence, the number of assembly direction changes, and the number of assembly tool changes as evaluation indicators, a fitness function is constructed. An improved particle swarm optimization (IPSO) approach for ASP is developed based on the peculiarities of the ASP issue. Redefining particle positions, velocities, and their update operations, and introducing mutation operators in genetic algorithm (GA) to improve the ability of PSO algorithms to jump out of local optima. Furthermore, the algorithm's convergence speed is enhanced by adjusting the value of the inertia weight. Finally, an example is provided to demonstrate the IPSO algorithm's usefulness and efficiency.

Keywords: Assembly Sequence Planning, Improved Particle Swarm Optimization, Interference Matrix, Genetic Algorithm
Grants and funding:

This work was supported by the Natural Science Foundation of Xiamen (3502Z20227432), Xiamen City University High Level Talent Research Funding Project (G2Q2022-7)

Received: April 12, 2023; Revised: July 21, 2023; Accepted: July 24, 2023; Prepublished online: July 24, 2023; Published: September 5, 2023  Show citation

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Zhang W. Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm. Manufacturing Technology. 2023;23(4):557-563. doi: 10.21062/mft.2023.056.
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