Abstract
This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0.40 - 1.56% for the BGP model and 4.49 - 7.52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications.
Highlights
Most of the automated welding systems were developed and used for mass production
The sulfur and oxygen content could vary in a wide range between the manufacturing batches, which influence the surface tension of the melted metal, an important factor defining the shape of the solidified surface [4], [5]
PROPOSED METHODS Our proposed method provides a bead geometry model on single weld beads in a horizontal position using fuzzy systems, and an optimizer to retrieve a list of welding process variables (WPVs) producing a specified weld bead geometry (WBG)
Summary
Most of the automated welding systems were developed and used for mass production. The transition from manual to robotized welding requires the adaptation of the welder’s expertise to the automated system. This knowledge is mostly not available in a quantified format as precisely as needed to program the robotic welding system or estimate the weld bead geometry (WBG). The associate editor coordinating the review of this manuscript and approving it for publication was Min Xia. The Tungsten Inert Gas (TIG) welding method can produce solid and high-quality joints for a wide range of regular and more exotic types of metals [1]. The deposition efficiency needs to be considered besides the inconsistent evaporation of the filler metal [6]
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