An Elitism Based Genetic Algorithm for Welding Sequence Optimization to Reduce Deformation

This paper reports the development and implementation of a Genetic Algorithm (GA) based on welding sequence optimization in which a structural deformation is computed as a fitness function. Moreover, a thermo-mechanical finite element analysis (FEA) was used to predict deformation. Elitism selection approach has been used to ensure that the three best individuals are copied over once into the next generation to expedite convergence by preserving qualified individuals having the potential of generating optimal solution. We exploited a sequential string searching algorithm into single point crossover method to avoid the repetition of single beads into the sequence. We utilized a bit string mutation algorithm by changing the direction of the welding from one bead chosen randomly from the sequence to avoid the repetition of the weld seams in the sequence. We computed the minimum number of iterations required for elitism GA based on the general Markov chain model of GA. Welding simulation experiments were conducted on a typical widely used mounting bracket which has eight seams using well-known software Simufact R ©. Simulation results were validated through a experiment and a fair amount of agreement was achieved in terms of deformation pattern. This algorithm allowed the reduction up to (∼80%). Finally elitism-based GA effectively reduces the computational complexity over exhaustive search.