Abstract
Drilling is the most important operation in aeronautic industry carried out previous to riveting. Its main problem lies with the burrs. Nowadays, there is a burr elimination task (manual task) subsequent to drilling and previous to riveting that increases manufacturing cost. It is necessary to develop a monitoring system to detect automatically and on-line when the generated burr is out of aeronautic limits, and then deburring. This system would reduce holes deburring to the holes which really are out of tolerance limits, focusing in trying to avoid false negatives. The article shows an improvement in burr generation prediction, using Data Mining techniques versus current mathematical model. It gives an overview of the process from data preparation and selection to data analysis (with machine learning algorithms) and evaluation of the models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hernández, J., Ramírez, M.J., Ferri, C.: Introducción a la Minería de datos. Pearson Prentice Hall, London (2004)
Mitchell, T.M.: Machine Learning. McGraw Hill International Editions, New York (1997)
Langley, P.: Areas of Application for Machine Learning. In: Proceedings of 5th Symposium on Knowledge Engineering, pp. 51–58 (1992)
Langley, P., Simon, H.A.: Applications of Machine Learning and Rule Induction. Communications of the ACM (1995)
Hond, D., Mannila, H., Smyth, P.: A Principles of data mining. Bradford Book (2001)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data monitoring to knowledge discovery in databases. AI magazine (1996)
Mitchell, T.M.: Does Machine Learning Really Work? AI Magazine 18(3), 11–20 (1997)
Kazakov, D., Kudenko, D.: Machine Learning and ILP for MAS. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 246–270. Springer, Heidelberg (2001)
Nilsson, N.J.: Introduction to machine Learning. Early draft of proposed textbook (1996)
Witten, I.H., Frank, E.: Data Mining: Practical Machine learning Tools and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreiro, S., Arana, R., Aizpurua, G., Aramendi, G., Arnaiz, A., Sierra, B. (2009). Data Mining for Burr Detection (in the Drilling Process). In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_188
Download citation
DOI: https://doi.org/10.1007/978-3-642-02481-8_188
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02480-1
Online ISBN: 978-3-642-02481-8
eBook Packages: Computer ScienceComputer Science (R0)