Prediction of friction stir weld quality without and with signal features

Abstract

Building a reliable prediction model can mitigate the need for actual experiments, hence saving time and cost. To this end, this study presents a methodology to predict weld quality for a particular friction stir weld configuration using machine learning and metaheuristic algorithms including K-nearest neighbor (KNN), fuzzy KNN (FKNN), and the artificial bee colony (ABC). The ABC algorithm was utilized to determine the best (F)KNN model with optimal K value and feature subset. First, models were built based on only experimental conditions including spindle rotational speed, plunge force, and feed rate, as well as derived values including a speed ratio and an empirical force index (EFI). The best model was identified to be 1-NN comprised of three features, i.e., rotational speed, feed rate, and EFI, with 93.16% classification accuracy based on leave-one-out cross-validation. The majority of data points leading to error were found to lie mostly on the boundaries between classes. It was shown that classification error could be reduced by removing those points, which is cheating and not recommended. Instead, it is recommended to improve classification accuracy without omitting dissenting data by introducing additional information to better distinguish misclassified data points. To this end, wavelet energy features extracted from weld signals of X-Force, Y-Force, spindle rotational speed, feed rate, and plunge force were added to the original feature pool. In order to determine the impact of each weld signal feature set, each signal feature set was individually tested. After applying ABC to the expanded feature pool to build the best model, perfect classification accuracy was achieved in several cases. The results suggest that adding signal features can greatly improve the effectiveness of model predictability of friction stir weld quality.