Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Abstract

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

“…Output: 0 for fracture at upper location and 1 is for fracture at weld Training data: 22 data points Testing data: not mentioned Validation data: validation dataset not used [59] In this method wavelet, a packet is used to obtain the temperature signal components of different frequency bands; then, a LSVM (least squares linear system as a loss function) is used as model for classification and parameter optimization using genetic algorithm (1) Able to replace the inequality restraints of an SVM which helps in learning fast (2) Very efficient for large scale and less complex problems (1) Inefficient for large scale and less complex problems Input: 3 features; temperature, rotational speed, and transverse speed Output: one of three classes of coefficient of strength greater than 75%, between 65% and 75%, and less than 65% Training data: 16 data points Testing data: not mentioned Validation data: validation dataset not used [61] A logistic model tree (LMT), which took in statistical data corresponding to vibrations from an accelerometer to classify the weld into three classes, namely, good, broken, and air bubble (1) LMTs combine the best of logistic regression and decision trees to give an accurate model (2) Since there are a lot of features out of which many could be useless, the decision tree part of the algorithm helps in feature selection, while the logistic regression part does the classification The calculations done to get the weights and trees are very complex, which causes a small change in data to drastically modify the architecture, or the output, which ends up wasting a lot of computing resources Input: statistical data from the sequential readings from an accelerometer; the mean, median, mode, standard deviation, skewness, variance, maximum, minimum, and count.…”

“…Maintaining the optimal temperature of the weld pool is essential to maintaining a strong weld. Using input parameters such as weld speed, tool rotational speed, and tool angle, an SVM is able to predict the maximum temperature a weld could reach which is invaluable information for a researcher or manufacturer [60]; a modified version of SVM known as LSVM is also used to obtain temperature signals of different frequency bands [61].…”

Effect of Natural Aging on Cold Forming Performance of 2219 Aluminum Alloy

“…2219 aluminum alloy is one of the most commonly used alloys for advanced applications, dues to its excellent weldability, good mechanical properties, high specific strength, and good corrosion resistance [1][2][3]. The thin sheets made of this alloy are used to form thin-walled components, which are widely applied in the aerospace, aviation, and automotive industries [4].…”