This study focuses on optimizing MIG welding parameters to achieve maximum joint strength using a
combined Taguchi and regression-based analytical approach. Mild steel specimens were welded using
a controlled set of current, voltage, and wire feed rates arranged through an L9 orthogonal array.
Tensile testing provided the primary performance metric, while graphical analysis, 3D interaction
mapping, and statistical modelling helped identify how each parameter influenced weld performance.
Results showed that welding current had the strongest positive effect on strength, followed by wire feed
rate, while voltage acted mainly as a stabilizing factor. Regression modelling further confirmed these
trends, offering prediction capability for unseen parameter combinations. The combined interpretation
revealed that high current, moderate voltage, and higher wire feed produced the most reliable and
strongest welds. The study demonstrates that pairing Taguchi optimization with regression analysis
provides a robust pathway for improving weld quality in industrial settings.
