Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation

Abstract

In the automotive industry, applying aluminum alloys to car chassis have become an important concern in order to reduce car weight. In aluminum laser welding, the strength of weld is typically reduced by porosity, underfill, and magnesium loss. In order to overcome these problems, laser welded with filler wire was suggested. In this study, experiments on the laser welding AA5182 of aluminum alloy with AA5356 filler wire were performed with respect to laser power, welding speed, and wire feed rate. The experiments showed that the tensile strength of the weld was higher than that of the base material under certain conditions. Using the experimental results, a neural network model was proposed to predict the tensile strength. To optimize the process parameters, a fitness function was formulated, taking into account weldability and productivity. A genetic algorithm was used to optimize the laser power, welding speed, and wire feed rate. The optimal value of these parameters was considered to be the proper process conditions in terms of weldability and productivity.