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.
A gap is generated in the weld joint due to dimensional error of welded parts and thermal deformation in aluminum alloy’s gas metal arc welding (GMAW) process of aluminum alloy. The optimum welding conditions corresponding to different gaps in the weld joint are required in the field. In this study, the welding conditions were optimized using Taguchi’s design of experiment method in response to gaps. Al5083-O with a thickness of 4.0 mm was used as the base material, and GMAW was performed on the T-fillet joints. An alternating current (AC) pulse was used for the welding process, and the welding experiment was performed for different gap sizes. Three levels of wire feed rate (WFR), electrode negative ratio (EN ratio), and teaching point (T.P) were selected as welding parameters, and 3 gap sizes (0, 0.5, and 1.0 mm) were selected as noise factors. Other welding conditions were fixed at a welding speed of 40 cm/min, a work angle of 40 degrees, a push of 10 degrees, the contact tip to work distance (CTWD) of 15 mm, and a shielding gas of 100 % Ar. The weld sizes such leg length, penetration depth, and throat thickness were measured using the cross-section. The maximum weld size that satisfies the minimum heat input was selected as the target value. Based on signal-to-noise ratio analysis, WFR, EN ratio, and T.P were selected as optimal levels of 11 m/min, 20 %, and 0 mm, respectively.
When the gas metal arc welding (GMAW) process is applied in the shipbuilding and heavy equipment industries, it is important to increase the amount of welding on the weld without defects. Application of the tandem GMAW process can reduce the man-hours increased owing to multipass and increase productivity. In shipbuilding and heavy equipment industry sites, the weldments are long and large; thus, a carriage-type bed is generally used. Accordingly, the overall lengths of the welding cables in the welding system are increased; thus, arc voltage drop occurs owing to the load voltages generated in the welding cables, and a robust bead cannot be obtained owing to spatters caused by short circuits. In this study, the voltage drop caused by the increase in cable lengths was compensated using a developed welding machine. By compensating for the voltage drop, it was possible to obtain a good quality bead by reducing the occurrence of spatter caused by short circuit. As a result of performing one pass welding using the asynchronous tandem GMAW process and the developed welding machine, it was possible to secure sufficient amount of welding required in the field and to obtain a sound bead appearance.
In gas metal arc welding (GMAW), the weld bead shape is an important factor that is directly related to the weld quality of welded joints. This study investigates the effects of process parameters, including welding speed (WS) and leading and trailing wire feed rates (WFR), on the weld bead shape, including the leg length and penetration depth, in the tandem GMAW of aluminum 5083-O alloy. An asynchronous direct current–direct current pulse tandem GMAW system and a tandem GMAW torch were designed and applied to improve welding productivity and welding quality. Response surface methodology was used to analyze the effects of the process parameters on the weld bead shape and to estimate regression models for predicting the weld bead shape. As a result of observing arc behavior using a high-speed camera, it was confirmed that the leading WFR affects the penetration depth and the trailing WFR affects the leg length. The coefficient of determination (R2) of the regression models was 0.9414 for the leg length and 0.9924 for the penetration depth. It was also validated that the estimated models were effective in predicting the weld bead shape (leg length and penetration depth) representative of weld quality in the tandem GMAW process.
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