Multi-objective optimization of a welding process by the estimation of the Pareto optimal set

The joining of Advanced High Strength Steel (AHSS) Martensitic type is being introduced in automotive industry; however, the optimization of the welding process is required to meet customer quality requirements. Two neural networks are built for modeling the relationship between the welding parameters and the output response of the process. An evolutionary algorithm is used for multi-objective optimization considering the neural networks as objective functions. The results consist of a set of solutions that approximate the Pareto optimal set. The related response of this set is known as the Pareto front. The set of solutions are validated in the real process satisfying the security and quality requirements.

► Our proposal neural network + multi-objective Evonorm approach represents an easy way to optimize complex industrial processes.