Gas metal arc welding of aluminum 5083 alloys was performed using three new welding wires with different magnesium and manganese contents and compared with commercial aluminum 5183 alloy filler wire. To investigate the effect of magnesium and manganese contents on the mechanical properties of welds, mechanical properties were evaluated through tensile strength, bending, and microhardness tests. In addition, the microstructure and chemical composition were analyzed to compare the differences between each weld. The tensile strengths of welds using aluminum alloy filler wires with a magnesium content of 7.33 wt.% (W1) and 6.38 wt.% (W2), respectively, were similar. The tensile strength and hardness of welds using wires with a similar magnesium content, but a different manganese content of 0.004 wt.% (W2) and 0.46 wt.% (W3), respectively, were higher in the wire with a high manganese content. Through various mechanical and microstructural property analyses, when the magnesium content of the filler wire was 6 wt.% or more, the manganese content, rather than the magnesium content, had a dominant effect on the strengthening of the weld.

Weld shape and size generally determine the quality of gas metal arc welding. Auto parts manufacturers prescribe the size and shape of the weld because they can indicate the mechanical properties of the weld. It is impossible to evaluate the quality of all welds through destruction inspection. Therefore, research on welding quality inspection using laser vision sensors as a non-destructive inspection method is underway. Although the external profile of the weld can be measured using a laser vision sensor, studies to predict the weld strength are insufficient. In this study, an artificial neural network (ANN) model was developed to predict the welding strength of the lap-fillet weld of an aluminum alloy. Input date for weld size was obtained in two ways. In the first method, a bead profile was acquired using a laser vision sensor, whereas the size of the weld was obtained through the acquired bead profile. In the second method, the size of the weld was obtained directly from cross-section analysis. The output data on the strength of the weld was obtained through a tensile shear test. Two models for predicting the tensile shear strength based on ANN were developed. By predicting the tensile strength of both models, the average error rate was within 10%, but the prediction accuracy using the laser vision sensor was better than that of the cross-sectional method.

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