Mechanical Characterization of SMAW and TIG Welded Joints Using Machine Learning Techniques

This research integrates experimental testing and Machine Learning (ML) techniques to predict the weld quality of Tungsten Inert Gas (TIG) and Shielded Metal Arc Welding (SMAW). A balanced dataset comprising weld parameters and mechanical test results including tensile strength, impact energy, and bend test outcomes was compiled for mild steel and stainless steel specimens with thickness ranging from 6mm to 10mm. Experimental results revealed that TIG welding produced superior tensile strength (up to 572 MPa) and impact energy (up to 58J) compared to SMAW. A Random Forest classifier achieved 100% accuracy in classifying weld quality as Good or Defect, while linear Regression produced tensile strength with an R2 of 0.68, Mean Absolute Error (MAE) of 17.5 MPa, and Root Mean Squared Error (RMSE) of 20.27 MPa. These results confirm the viability of ML techniques as non-destructive tools for weld quality prediction and mechanical property estimation. The framework developed in this research contributes to intelligent welding process control and supports the transition toward efficient, data driven manufacturing.