| Summary: | The prediction of the weld bead geometry parameters is an important aspect of welding processes due to it is related to the strength of the welded joint. This research focuses on using statistical design techniques and a deep learning neural network to predict the weld bead shape parameters of shielded metal arc welding (SMAW), metal inert gas (MIG), and tungsten inert gas (TIG) welding processes. With the statistical design techniques, experiments were carried out to obtain the data for generating the regression models. Establishing mathematical models that shows the relationship between welding process parameters and weld bead size is significant for practical applications. The mathematical model enables the determination of the weld bead size when setting specific welding process parameters. In this research, experimental research results were obtained to build mathematical models showing the relationship between welding process parameters and weld bead geometries for SMAW, MIG, and TIG welding processes. The research results serve as the basis for establishing predictive systems or optimizing welding process parameters. With deep learning neural network techniques, we developed an artificial intelligence-based system for predicting complicated relations between the welding process parameters and the weld bead size. Both a regression model and the deep learning model result in a good correlation between the welding process parameters and the weld bead geometry.
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