A Fixture Assembly Sequence Planning Method Based On Particle Swarm Algorithm

With increasing market competition and economic globalization acceleration, for domestic and international manufacturing industries, enterprises are facing the great challenges of productivity, cost, and competitiveness. Digital product assembly planning is used to determine viable and efficient assembly sequences, and ensure the feasibility and optimality of the actual assembly process. It further shortens product development cycles, improves product quality, reduces production costs, shortens production cycle, and is of great importance to capture market as soon as possible. Fixtures are essential processing units. Rapid fixture assembling has also become an important step to production costs.Particle Swarm Optimization (PSO) algorithm is a typical swarm intelligence optimization algorithm to simulate the individuals self-learning behaviors and social behaviors of mutual cooperation among individuals of foraging bird groups and other animals. It is scalable, simple, and easy to implement. In the paper, PSO algorithm is used to solve fixture assembly sequence planning problem. First, according to the analysis and extraction of fixture assembly information, a complete and correct fixture assembly model is established that can be direct used in PSO algorithm in the format of matrixes, including assembly direction matrix, interference matrix, assembly sequence relation matrix, etc. Then, in Matlab environment, taking the shortest assembly time as the objective, the feasible assembly sequences for specific fixture assembly examples are obtained using PSO algorithm and the optimal assembly sequence is found. Finally, the influence of main factors on PSO algorithm is analyzed. With the increase of population, the chance to find the optimal solution increases. When ω and c1 increase and c2 decreases, it is good for global searches of the PSO algorithm. When ω and c1 decrease, and c2 increases, it is good for local searches of the PSO algorithm. In practical applications, the factors should be adjusted according to specific problems.