This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead’s local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a
multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by
comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width,
contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear
regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight Vgroove
by comparing the estimated bead characteristics with the measurements and calculating the accumulated
error of the filled groove cross-section. The results show that the proposed model provides a good estimation of
the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low
error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects
