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Fastening is a recurrent assembly operation at the aerospace industry. Among the many different types of fasteners being used blind ones offer particular advantages but there is yet a lack of reliable installation monitoring methods for their massive adoption. The present paper proposes an installation evaluation solution for blind fasteners that integrates the effect of the previous drilling operation and allows the visualization of the relationships between hole quality parameters, installation variables and installation quality. The results show high precision values of 0.95 and accuracy of 0.9.

Due to technological advancements, many manufacturing processes are now real-time monitored through sensors that provide continuous signals of the process parameters rather than providing simpler point observations of the process response. Signals (profiles) obtained through these sensors can reveal important information about the quality of the process being monitored. In this work, we propose a general predictive control framework for on-line process quality monitoring where data is available in the form of a profile. The proposed framework is an integration of ideas from classical on-line process control and advanced machine learning techniques, namely, Random Forests. The proposed framework has the advantages of being more interpretable compared to other methods found in the literature, and has the flexibility to include several commonly used transformations of the signal as features. In addition, abnormal out of control signal characteristics of the process known from experience by operators can be easily incorporated in the random forest technique. An illustration of the proposed framework applied to the case of a tapping manufacturing process is provided. Model comparison results show a superior performance of the proposed framework over previously proposed monitoring methods for the considered tapping process. From a receiver operating characteristic curve analysis, it was found that an area under the curve (AUC) of 0.923 was achieved by the proposed model compared to an AUC of 0.867 for the Generalized Variance model proposed in the literature.

Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset., Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Governmentand project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y Le ́on, both co-financed through European-Union FEDER funds and project IT-2005/00201 of the INNOTEK Program of the Basque Government