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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.

A low-frequency-assisted boring operation is a key cutting process in the aircraft manufacturing sector when drilling deep holes to avoid chip clogging based on chip breakage and, consequently, to reduce the temperature level in the cutting process. This paper proposes a predicted force model based on a commercial control-supported chip breaking function without external vibration devices in the boring operations. The model was fitted by conventional boring measurements and was validated by vibration boring experiments with different ranges of amplitude and frequency. The average prediction error is around 10%. The use of a commercial function makes the model more attractive for the industry because there is no need for intrusive vibration sensors. The low-frequency assisted boring (LFAB) operations foster the chip breakage. Finally, the model is generic and can be used for different cutting materials and conditions. Roughness is improved by 33% when vibration conditions are optimal, considered as a vibration amplitude of half the feed per tooth. This paper presents, as a novelty, the analysis of low-frequency vibration parameters in boring processes and their effect on chip formation and internal hole roughness. This has a practical significance for the definition of a methodology based on the torque model for the selection of conditions on other hole-making processes, cutting parameters and/or materials. This research was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723698 (ForZDM).

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process. This research was funded by the vice-counselling of technology, innovation and competitiveness of the Basque Government grant agreements IG-2011/0000392, ZL-2019/00720 (HARDCRAFT project) and KK-2019/00004 (PROCODA project). The work done in this paper has also received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723698 (ForZDM). Besides, this project, called IDI-20100674, was supported by the Centre for the Development of Industrial Technology answering to the Ministry of Economy, Industry and Competitiveness (Spanish Government).

Threading holes using tapping tools is a widely used machining operation in the industry. This manufacturing process involves a great tool immersion in the part, which involves both friction and cutting. This makes the use of coatings critical to improving tool life. Four coatings are used based on Physical vapor deposition (PVD) technology&mdash, TiN, TiCN, TiAlN and TiAlN+WC/C are compared to uncoated tool performance. The effect of various coatings on the life of M12 &times, 1.5 tapping tools during threading of through holes 20 mm deep, in GG25 casting plates, dry and applying cutting speed of 50 m/min. The end-of-life criterion has been established based on a cutting torque of 16 N-m. Taking the uncoated tap as a basis for comparison, it is observed that coatings based on PVD technologies increase tool life doubling in the most advantageous case with the TiAlN coating. PVD type coatings provide better protection to wear at cylindrical area of the tool, where the thread profile is finished, than uncoated taps. The teeth located in the cone-cylinder transition zone of the taps suffer the most wear regardless of the coating. However, taps coated with TiAlN+WC/C wear level values is lowest of all the coatings tested, which indicates a strong reinforcement in these teeth.

Thread quality control is becoming a widespread necessity in manufacturing to guarantee the geometry of the resulting screws on the workpiece due to the high industrial costs. Besides, the industrial inspection is manual provoking high rates of manufacturing delays. Therefore, the aim of this paper consists of developing a statistical quality control approach acquiring the data (torque signal) coming from the spindle drive for assessing thread quality using different coatings. The system shows a red light when the tap wear is critical before machining in unacceptable screw threads. Therefore, the application could reduce these high industrial costs because it can work self-governance. This research was funded by the vice‐counseling of technology, innovation and competitiveness of the Basque Government grant agreements IT‐2005/00201, ZL‐2019/00720 (HARDCRAFT project) and KK‐2019/00004 (PROCODA project).

Tapping is an extensively employed manufacturing process by which a multi-teeth tool, known as tap, cuts a mating thread when driven into a hole. When taps are new or slightly worn the process is usually in control and the geometry of the resulting threads on the work piece is correct. But as the tap wear increases the thread geometry deviates progressively from the correct one and eventually the screw threads become unacceptable.The aim of this paper consists on a development of statistical process control strategy for decision making according to data coming from the current signal of the tap spindle for assessing thread quality. It could operate on line and indicates when the tap wear is so critical that, if the process were continued, it would result in unacceptable screw threads. The system would be very cost-effective since the tapping process could be run without any operator intervention.

After some years of practice, common misunderstandings and lack of conceptual organization/structure have found from the learning results of Industrial Drawing students. This article presents an experimental work developed in order to measure the learning process through the concept map analysis. The main aspects to investigate are map complexity, the influence of mapping individual or collectively and the perception of the students on the usefulness of the activity. The complexity level of the maps is set extracting qualitative data and evaluating it through a rubric. The data studies the performance of individual or group working dynamics. In order to evaluate the perception of the students about the mapping activity, a survey presents the results obtained. The present work can conclude that: the students are able to identify and organize main and secondary concepts but their ability to represent a complex system is weak. Individually drawn maps show slightly higher average rate than the ones done in groups and parallel to this, the survey shows a higher usefulness appreciation of the tool from the students that have been mapping individually. The purpose with highest rate is the one related to Information Management and Collective Work. Finally, the use of concept maps improved overall from 0,8 to 1 point the tests scores, compared to the students that did not use this tool.

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

Drilling operation of dissimilar stack materials is a crucial assembly operation in airframe manufacturing; this is due to both the number of holes drilled in an aircraft structure and the fact that this is an operation performed in the finishing phase of the manufacturing chain. This paper aims to evaluate an alternative strategy for conventional drilling and peck drilling, which are now the most widespread solutions used for drilling aluminium alloys. The alternative approach proposed by this paper consists of low frequency assisted drilling (LFAD) performed into frequencies vibrations (between 50 to 100 Hz). In this paper, the chip formation process of drilling assisted by low-frequency vibrations of FC/Al stack material has been analytically modelled and compared with the conventional drilling of aluminium. Results show chip segmentation during the drilling operation resulting in less temperature increasing, avoiding problems in the final geometrical quality of the hole, and burr formation.

Tapping by cutting is one of the most common operations in manufacturing. It consists of cutting internal threads on the wall of a previously drilled hole by means of a tool called a tap that has cutting edges on its chamfered periphery. When tap wear is slight the process and geometry are usually in control and correct, respectively. However, when tap wear level is high the geometry is not correct and the process is out of control. This is an unacceptable industrial cost. The aim of this paper consists on an industrial monitoring application to data coming from the current/torque signal of the tap spindle for assessing thread quality. It operates in real time and indicates when the tap wear is so critical that, if the process were continued, it would result in unacceptable screw threads. Then the system shows a red light so that the operator could replace the worn-out tap. The system would be very cost-effective since the tapping process could be run without any operator intervention.