Abstract
The short circuit gas metal arc welding is widely used in the industry, having an important role in manufacturing processes. In order to achieve high-quality control levels in weldments, researches are carried out aiming to find a way to monitor the quality of weldments in real time. In this context, this work proposes to use a type-1 and singleton fuzzy logic system classifier to identify the gas flow rate in short circuit gas metal arc welding. The investigated dataset was performed in laboratory, consisting of the current and voltage signals related with weld beads, for different gas flow rates. In addition, the feature extraction uses the statistics from the data signal and a criterion to quantify the metal transfer stability in short circuit processes, denominated Laprosolda Criterion. Based on the excellent performance of the model, the proposal is suitable to be used in welding quality monitoring.
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