Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Abstract

Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

“…In Hartl et al [15], the focus was on the indirect monitoring of the surface quality. Various sensors were employed for the inline acquisition of accelerations, forces, the spindle torque, and temperatures.…”

“…For the RNN, the instantaneous frequency [19] and the spectral entropy, which are also often used as a feature in medicine signal processing [20], were determined and employed as input. For the CNN, spectrograms were generated, similar to Hartl et al [15]. Spectrograms depict the spectral density of a signal depending on the time and the frequency in a three-dimensional manner [21].…”

“…been demonstrated to be adequate in previous studies [15]. The allocation of the ROI to the three data sets and the initialization of the weights of the ANNs were conducted randomly.…”

Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization

“…Important process parameters to control the surface topography of friction stir welds are the welding speed and the rotational speed of the tool [22]. Hartl et al [23] presented key indicators for quantifying the surface topography of friction stir welds and showed that some of these can be predicted by evaluating process variables such as the process forces or temperatures [24].…”

Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

“…Mishra et al [12] predicted the corrosion potential of friction stir welded joints by using back propagated artificial neural network. Hartl et al [13] used artificial neural networks for the surface quality of friction stir welded joints. Rehim et al [14] predicted and simulated the Vickers hardness of friction stir welded joints by using artificial neural network.…”