The geometric integrity of the fusion zone (FZ) is a critical determinant of weld quality in Tungsten Inert Gas (TIG) welding. This study presents a comparative analysis of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) for modeling and predicting FZ width geometry in AISI 1020 mild steel weldments. Experiments were conducted using a Central Composite Design (CCD) comprising 30 runs, varying current, voltage, travel speed, and gas flow rate. A quadratic regression model was developed via RSM, yielding a high coefficient of determination (R2 = 0.9765) and adequate precision (27.7872), indicating strong statistical fit within the design space. Concurrently, a feedforward backpropagation ANN (4-10-1 architecture) was trained using the Levenberg-Marquardt algorithm, achieving an overall R2 of 0.889. While RSM demonstrated superior interpolation accuracy for the experimental domain, the ANN model exhibited robust capability in capturing complex nonlinearities and generalizing across test subsets. The results confirm that both models are viable for process optimization, with RSM offering explicit mathematical relationships within the experimental domain and ANN providing flexible nonlinear mapping with strong generalization capability for unseen process conditions.
Cite this paper
Olejieme, G. T. , Achebo, J. I. , Etin-Osa, C. E. and Achebo, E. P. J. (2026). Comparative Analysis of Artificial Neural Network and Regression Modeling for Predicting Fusion Zone Width in TIG Welding. Open Access Library Journal, 13, e15559. doi: http://dx.doi.org/10.4236/oalib.1115559.
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