A Study of Process Parameters Optimization Using Genetic Algorithm for Nd:YAG Laser Welding of AA5182 Aluminum Alloy Sheet

AA5182 알루미늄 판재의 Nd:YAG 레이저 용접에서 유전 알고리즘을 이용한 공정변수 최적화에 대한 연구

Many automotive companies have tried to apply the aluminum alloy sheet to car body because reducing the car weight can improve the fuel efficiency of vehicle. In order to do that, sheet materials require of weldablity, formability, productivity and so on. Aluminum alloy was not easy to join these metals due to its material properties. Thus, the laser is good heat source for aluminum alloy welding because of its high heat intensity. However, the welding quality was not good by porosity, underfill, and magnesium loss in welded metal for AA5182 aluminum alloy. In this study, Nd:YAG laser welding of AA 5182 with filler wire AA 5356 was carried out to overcome this problem. The weldability of AA5182 laser welding with AA5356 filler wire was investigated in terms of tensile strength and Erichsen ratio. For full penetration, mechanical properties were improved by filler wire. In order to optimize the process parameters, model to estimate tensile strength by artificial neural network was developed and fitness function was defined in consideration of weldability and productivity. Genetic algorithm was used to search the optimal point of laser power, welding speed, and wire feed rate.