High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning

•An extensive industrial dataset of threads processed with different coated tools.•A new approach to predict threads quality considering tool coating and torque signals.•Different machine-learning techniques for balanced & imbalanced datasets were tested.•Ensembles are the most accurate, easily-optimized & industrially-applicable models.