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Maintenance is going through to major changes in a lot of activity fields where the current maintenance strategy must adjust to the new requirements. The aeronautics industry belongs to one these activity fields which are trying to carry out important changes around its maintenance strategy. It needs to minimize the cost for the maintenance support...
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Citations
... The aeronautic industry, along with other industrial sectors, has to modify some of its manufacturing processes and its current maintenance strategy. Regarding the maintenance strategy, in order to substitute the traditional corrective maintenance for one which is preventative and predictive, it is necessary to reduce the cost as much as possible and to increase the operative reliability, as explained in Ferreiro and Arnaiz [9]. As for manufacturing, the main need is to increase productivity and optimise certain processes, such as drilling, at the same time guaranteeing the quality of the product. ...
This paper presents a particular problem dealing with the apparition of burr during the drilling process in the aeronautic industry. This burr cannot exceed a height limit of 127 μm as set out by the aeronautical guidelines and must be eliminated before riveting. If this is not performed, it can cause structural damage which would constitute a danger due to the lack of safety. Moreover, the industry needs to find an automated and optimised process in which the drilling and deburring can be carried out in real time, eliminating those other unnecessary tasks, in order to obtain high-quality pieces. The work presents the applicability of data mining and machine learning techniques so as to obtain a real time burr detection model. This model could be implanted in the computer numerical control of the machine allowing the whole process to be automated and optimised. These techniques can be applied to other types of processes.
... Aeronautic industry, as well as other industrial sectors must modify some of its manufacturing processes and maintenance strategy. Considering maintenance strategy, it is necessary to minimize the cost of maintenance and to increase operational reliability, replacing the traditional ''fail and fix'' method with ''predict and prevent'' as explained in Ferreiro and Arnaiz (2010). And with regard to manufacturing, the major need is to increase productivity and to optimize and automate certain processes while ensuring the quality of the product. ...
Drilling process is one of the most important operations in aeronautic industry. It is performed on the wings of the aeroplanes and its main problem lies with the burr generation. At present moment, there is a visual inspection and manual burr elimination task subsequent to the drilling and previous to the riveting to ensure the quality of the product. These operations increase the cost and the resources required during the process. The article shows the use of data mining techniques to obtain a reliable model to detect the generation of burr during high speed drilling in dry conditions on aluminium Al 7075-T6. It makes possible to eliminate the unproductive operations in order to optimize the process and reduce economic cost. Furthermore, this model should be able to be implemented later in a monitoring system to detect automatically and on-line when the generated burr is out of tolerance limits or not. The article explains the whole process of data analysis from the data preparation to the evaluation and selection of the final model.
One of the most important processes in the aeronautical sector is drilling. The main problem associated with drilling is burr.
There is a tolerance level for this burr and it cannot exceed 127 microns, which would provoke structural damage and other
problems. Currently, the burr elimination task is carried out visually and manually with the aim of guaranteeing quality in
the process. However, it is an expensive procedure and needs to be replaced by a motorized system capable of automatically
detecting in which holes the burr exceeds the permitted level and has to be eliminated or reduced. The paper presents a burr
prediction model for high speed drilling in dry conditions on aluminium (Al 7075-T6), based on a Bayesian network learned
from a set of experiments based on parameters taken from the internal signal of the machine and parameters from the condition
process. The paper shows the efficiency and validity of the model in the prediction of the apparition of burr during the drilling
and compares the results with other data-mining techniques.
