PT Journal
AU Zhang, W
TI Assembly Sequence Intelligent Planning based on Improved Particle Swarm Optimization Algorithm
SO Manufacturing Technology Journal
PY 2023
BP 557
EP 563
VL 23
IS 4
DI 10.21062/mft.2023.056
DE Assembly Sequence Planning; Improved Particle Swarm Optimization; Interference Matrix; Genetic Algorithm
AB 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.
ER