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A difficult task for the transport sector is to make its assemblies lighter and perform more efficiently. Use of aluminum and its alloys has increased extensively in this sector because of reduction in weight of the vehicles and resulting energy savings. High thermal conductivity and thermal expansion pose difficulty in welding of these alloys. Cold metal transfer (CMT), a low heat input welding process, is the best choice for welding of these alloys. However, controlling the welding input parameters is highly necessary to obtain defect-free and high strength welded joints. In the present study, an attempt is made to develop a Matlab software-based application by two approaches, such as multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), for predicting the complete weld bead shape (graphical representation) of AA5052 using the CMT welding process. The data inputs used for both approaches are welding current (A) and welding speed (mm/min), respectively. A graphical interface is built to help the user to choose welding input parameters and obtain directly a representation of the weld bead profile in graphical form. In addition, the output response shows the complete weld bead shape, which is defined by the X and Y coordinates of the various points in the weld bead profile. The results are validated with randomized tests against the weld bead shape predicted by Matlab. Comparatively, ANFIS is the more effective method for predicting the weld bead profile and shows better agreement with the experimental profile than MRA. Further, the reliability and stability of the ANFIS model were determined from the mean absolute error percentage, root mean square error values, and linear R2 fit model, confirming that the ANFIS-based prediction is in better agreement with the experimental values than MRA.

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

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Generally, in gas metal arc welding (GMAW) high heat input causes drastic changes in the microstructures of weldment (fusion zone and heat affected zone), which in turns affects the performance of the welded blanks during forming operation. The present study focuses on the parametric effects such as welding current, welding speed and torch orientation concerning welding direction on mechanical properties, microstructural characterization and formability of AA5052 Cold metal transfer (CMT) welded blanks (WB's). Based on the macrostructure images obtained from various trials (trial 19, 20 and 21, which is corresponding to Drag angle of 10°, 90° or Zero angle and Push angle of 10°, respectively) three were selected for further studies. The macrograph, microstructural evaluation, mechanical behavior and forming limit curve (FLC) of the WB's are examined for the selected parameters and for base metal (BM). The formability of the BM and WB's are investigated by obtaining FLC using Nakajima test. Of the three different torch orientation concerning welding direction, the WB made with 10° push angle yields the superior mechanical properties such as high tensile strength, increase in hardness and more bending strength than the remaining torch orientations. In addition, total elongation and formability are of concern; drag angle of 10° yields the better result, compared to the other torch orientations.