Optimization of geometric parameters in a welded joint through response surface methodology

- Experimental desing for a GMAW welding process to maximize the amount of information.
- Response surface-based modelling (RSM) to quantify response variables of interest.
- Statistical model selection to obtain the most informative models.
- Statistical model checking for definitive models to ensure inference capabilities.
- Multiobjective optimization to identify the Pareto front of optimal solutions.

This work makes use of experimental design and response surface methodology to model Gas Metal Arc Welding processes. The correlations among three key geometric parameters, ie., penetration, bead width and overthickness, and four technological variables that define the welding process are quantified. Based on experimental data and using model selection techniques, a mathematical model has been deduced for each of the response variables herein presented. Using these models, a multiobjective optimization is carried out to find the space of optimal solutions (i.e., the Pareto front). After a preliminary study of the relationships between independent and response variables, regression models are built. These models capture the data variability reasonably well (e.g., around 70% of the variability). These models are the basis to perform the multiobjective optimization using the ε-constraint approach. Results reveal that the conditions which favour a good balance between maximum penetration and minimum bead width and overthickness, involve a high value for gas flow rate, low values for electrode feed rate and voltage, and an intermediate value for the electrode position. This permits the authors to define the welding conditions that lead to an optimum joint geometry and then to guarantee its properties.