To enhance productivity and provide high quality production material in a GMA welding process, weld quality, productivity and cost reduction affects the number of process variables. In addition, a reliable welding process and conditions must be implemented to reduce weld structure failure. In various industries the welding process mathematical model is not fully formulated for the process parameter and on the welding conditions, therefore only partial variables can be predicted. The research investigates the interaction between the welding parameters (welding speed, distance between electrodes, and flow rate of shielding gas) and bead geometry for predicting the weld bead geometry (bead width, bead height). Taguchi techniques are applied to bead shape to develope curve equation for predicting the optimized process parameters and quality characteristics by analyzing the S/N ratio. The experimental results and measured error is within the range of 10% presenting satisfactory accuracy. The curve equation was developed in such a way that you can predict the bead geometry of constructed machinery that can be used for making tandem welding process.

Penetration control is an important factor in determining the weld quality in keyhole mode laser welding, which enables deep penetration. In this study, machine learning models and neural network models were developed by using 380 published welding data which were constructed for steel base metals under the following welding conditions: a laser power of 0.3-16.7 kW, a welding speed of 0.3-20.0 m/min, and a bead diameter of 0.05-0.78 mm. A machine learning model SVM (supported vector machine) could accurately predict the penetration depth with a coefficient of determination, R 2 of 0.95. A shallow neural network model with five nodes in only one hidden layer was developed with a slightly improved accuracy with R 2 of 0.98. It was confirmed that neither model was overfitted, and process parameters (welding speed and beam diameter) maps with penetration depth contours were provided for a laser power of 2-8 kW.

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