ABSTRACT

This report explores the possibility of using Genetic Algorithms (GAs) as a method to decide near-optimal settings of a MIG welding process. The problem was to choose the near-best values of three control variables (welding current, wire feed rate and wire diameter) based on four quality responses (Bead height, bead width, depth of penetration and Hardness number), inside a previous delimited experimental region. The search for the near-optimal was carried out step by step, with the GA predicting the next experiment based on the previous, and without the knowledge of the modeling equations between the inputs and outputs of the MIG welding process. The GAs was able to locate near-optimum conditions, with a relatively small number of experiments. However, the optimization by GA technique requires a good setting of its own parameters, such as population size, number of generations, etc. Otherwise, there is a risk of an insufficient sweeping of the search space

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