Fixture layout optimization in multi-station assembly processes using augmented ant colony algorithm

• In each iteration, arranging all the fixture layouts as the ascending order of the corresponding objective function value before the global search.
• In the next iteration, changing the expression of the new pheromone value of the ant for each fixture layout.
• Changing the mutation step size in the global search.
• Changing the limiting step size in the local search from a fixed value to a variable value with certain regularity.

In recent years, ant colony algorithms (ACAs) are used to solve the fixture layout optimization problem for a single workpiece machined in a single manufacturing stage. Assembly processes, however, are normally multi-station manufacturing processes, whose fixture layout optimization problem is much more complex. The purpose of this research is to develop an augmented ACA based on continuous optimization methods to optimize fixture layouts for 2D rigid parts in multi-station assembly processes. The algorithm is augmented by changing the mutation step size in the global search, the limiting step size in the local search, the new pheromone value's expression of the ant, etc. The augmented ACA is used to properly select the coordinates of two locating pins to minimize the sensitivity index. A case about three-station automotive side aperture assembly processes is studied to verify the effectiveness of the augmented ACA. The results show that the augmented ACA can generate more accurate results with a faster rate of convergence and a better stability than the basic ACA. This work could also be applied to fixture layouts optimization problems for 3D rigid parts in multi-station manufacturing processes.