Abstract
In serial production, problems are constantly encountered in the selection of welding parameters due to the excess of welding parameters and variations. In order to compensate for these variations, mostly high energy flux is used. In this study, an approach developed in order to estimate weld nugget diameter in determining the welding parameters for sheets with a thickness of 0.6-3 mm is introduced. Minitab statistical program was used to create experimental data and mathematical operations. First of all, 7 source parameters were selected and experimental design (DOE) was carried out for 64 experiments using the ½ partition factorial method in Minitab software. With the experiments, real weld nugget diameters were obtained. These results were transferred to the Minitab software and the mathematical model of the system was established. Weld nugget diameter estimation procedures were carried out using the experimental design (DOE) data. Test and prediction data were transferred to Minitab software, regression graph was drawn and R-Sq and R-Sq (adj) values were calculated. In addition, samples were created with randomly selected data for verification and comparison was made by transferring them to Minitab. According to the results of this study, remarkable accuracy rates have been achieved in the weld nugget diameter estimation with Minitab.
Highlights
Today, 7-12 thousand spot welding is used when a car is being produced
If the P-value for any variable is greater than the value specified in Alpha to remove, Minitab removes the variable with the largest p-value from the model, calculates the regression equation, displays the results, and initiates the step
The values of the weld nugget diameter in the model were estimated by increasing the values for each parameter from Minitab \ Stat \ design of experiments (DOE) \ Factorial \ Predict into [14,15,16]
Summary
7-12 thousand spot welding is used when a car is being produced. Electric Resistance Welding (ERW) is generally done by computer controlled robots. It can be seen that semantic rules are used to create accurate predictive models With this method, allow engineers to reduce design and process alternatives response parameter (weld nugget width) can be effectively analyzed and predicted by Kim et al [3]. With samples created under variable welding currents I, electrode forces F, welding times T, preheating currents IA, single-predictive optimization and spot break load estimation can be performed. Support vector machines using radial weld nugget, acceleration, and random forest techniques can generally achieve the best performance by Pereda et al [11] It includes weld adhesion, the influence of weld parameters on joint quality, major metallurgical defects in Al spot welds, and electrode distortion.
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