Artificial Neural Network Modeling for Prediction of Welding Transverse Distortion in TIG Welding of Mild Steel

This study investigates the application of Artificial Neural Networks (ANN) for predicting welding-induced transverse distortion in Tungsten Inert Gas (TIG) welding of 10 mm thick ASTM A36 mild steel plates. Experimental data from a Face-Centered Central Composite Design (FCCD) were used to train, validate, and test a feedforward ANN model with four input parameters (current, voltage, gas flow rate, and welding speed) to produce a single response output of transverse distortion. The ANN model demonstrated high predictive accuracy, achieving an overall R² value of 0.911 and a mean squared error (MSE) of 9.41 × 10⁻⁶. Training convergence was achieved within 5 epochs, with the gradient dropping to 8.89 × 10⁻⁹, well below the target threshold of 1 × 10⁻⁷. Error histogram analysis revealed residuals concentrated near zero (±0.0061 mm), confirming unbiased prediction behavior with a mean absolute error (MAE) of 0.018 mm. Regression analysis confirmed a strong correlation between predicted and experimental values, with 83% of predictions deviating by less than ±0.025 mm. The findings confirm ANN as an effective and robust tool for accurate pre-weld distortion forecasting, reducing trial-and-error iterations and supporting data-driven process optimization in structural welding applications.