Advertisement

Journal of Intelligent Manufacturing

, Volume 23, Issue 5, pp 1733–1743 | Cite as

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

  • Maciej Grzenda
  • Andres Bustillo
  • Pawel Zawistowski
Open Access
Article

Abstract

A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented. The input data includes cutting parameters and axial cutting force obtained from the power consumption of the feed motor of the milling centres. Two different coolant strategies are tested: traditional working fluid and Minimum Quantity Lubrication (MQL). The model is constructed in three phases. First, a new strategy is proposed to evaluate and complete the set of available measurements. The primary objective of this phase is to decide whether further drilling experiments are required to develop an accurate roughness prediction model. An important aspect of the proposed strategy is the imputation of missing data, which is used to fully exploit both complete and incomplete measurements. The proposed imputation algorithm is based on a genetic algorithm and aims to improve prediction accuracy. In the second phase, a bag of multilayer perceptrons is used to model the impact of deep drilling settings on borehole roughness. Finally, this model is supplied with the borehole dimensions, coolant option and expected axial force to develop a 3D surface showing the expected borehole roughness as a function of drilling process settings. This plot is the necessary output of the model for its use under real workshop conditions. The proposed system is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.

Keywords

Deep drilling Incomplete data Imputation MQL Surface roughness Multilayer perceptron 

Notes

Acknowledgments

This work has been made possible thanks to the support received from Nicolás Correa S.A. and Fundación Fatronik-Tecnalia, which provided the drilling data and performed all the experimental tests. The authors would especially like to thank Mr. Eduardo Elizburu, Mrs. Iraitz Etxeberria and Mr. Germán Rodríguez for their kind-spirited and useful advice.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

References

  1. Abdella, M., & Marwala, T. (2005). The use of genetic algorithms and neural networks to approximate missing data in database. IEEE 3rd international conference on computational cybernetics (pp. 207–212).Google Scholar
  2. Acuña, E., & Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy. In Classification clustering and data mining applications. Berlin: Springer.Google Scholar
  3. Batista, G. E. A. P. A., & Monard, M. C. (2001). A study of K-nearest neighbour as a model-based method to treat missing data. Proceedings of the Argentine symposium on artificial intelligence (pp. 1–9).Google Scholar
  4. Benardos P. G., Vosniakos G. (2003) Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture 43(8): 833–844CrossRefGoogle Scholar
  5. Biglari F., Fang X. (1995) Real-time fuzzy-logic control for maximizing the tool life of small-diameter drills. Fuzzy Sets and Systems 72(1): 91–101CrossRefGoogle Scholar
  6. Braga D., Diniz A., Miranda G., Coppinni N. (2002) Using a minimum quantity of lubrication and a diamond coated tool in drilling of aluminum-silicon alloys. Journal of Materials Processing Technology 122: 127–138CrossRefGoogle Scholar
  7. Breiman L. (1996) Bagging predictors. Machine Learning 24(2): 123–140Google Scholar
  8. Chandrasekaran M., Muralidhar M., Krishna C. M., Dixit U. S. (2010) Application of soft computing techniques in machining performance prediction and optimization: A literature review. International journal of advanced manufacturing technology 46(5–8): 445–464CrossRefGoogle Scholar
  9. Choudhary A. K., Harding J. A., Tiwari M. K. (2009) Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5): 501–521CrossRefGoogle Scholar
  10. Davim J. P., Sreejith P. S., Gomes R., Peixoto C. (2006) Experimental studies on drilling of aluminium (AA1050) under dry, minimum quantity of lubricant, and flood-lubricated conditions. Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture, Part B 220(10): 1605–1611CrossRefGoogle Scholar
  11. Dempster A. P., Laird N. M., Rubin D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39: 1–38Google Scholar
  12. Filipovic A., Stephenson D. A. (2006) Minimum quantity lubrication (MQL) applications in automotive power-train machining. Machining Science and Technology 10: 3–22CrossRefGoogle Scholar
  13. Gediga, G., & Düntsch, I. (2002). Maximum consistency of incomplete data via non-invasive imputation. Artificial Intelligence Review, 19(1), 93–107.CrossRefGoogle Scholar
  14. Hashmi K., Graham I., Mills B. (2000) Fuzzy logic based data selection for the drilling process. Journal of Materials Processing Technology 108(1): 55–61CrossRefGoogle Scholar
  15. Hayajneh N. T. (2001) Hole quality in deep hole drilling. Materials and Manufacturing Processes 16(2): 147–164CrossRefGoogle Scholar
  16. Heinemann R., Hinduja S., Barrow G., Petuelli G. (2006) Effect of MQL on the tool life of small twist drills in deep-hole drilling. International Journal of Machine Tools and Manufacture 46(1): 1–6CrossRefGoogle Scholar
  17. Heinemann R., Hinduja S., Barrow G. (2007) Use of process signals for tool wear progression sensing in drilling small deep holes. International Journal of Advanced Manufacturing Technology 33(3–4): 243–250CrossRefGoogle Scholar
  18. Hu, M., Salvucci, S.M., & Cohen, M.P. (1998). Evaluation of some popular imputation algorithms. Proceedings of the Survey Research Methods Section, American Statistical Association (pp. 308–313).Google Scholar
  19. Jantunen E., Vaajoensuu E. (2010) Self adaptive diagnosis of tool wear with a microcontroller. Journal of Intelligent Manufacturing 21(2): 223–230CrossRefGoogle Scholar
  20. Jönsson P., Wohlin C. (2006) Benchmarking k-nearest neighbour imputation with homogeneous Likert data. Empirical Software Engineering 11(3): 463–489CrossRefGoogle Scholar
  21. Juszczak, P., & Duin, R.P.W. (2004). Combining one-class classifiers to classify missing data. Multiple Classifier Systems (pp. 92–101).Google Scholar
  22. Kubota H., Tabei H. (1999) Drilling of a small and deep hole using a twist drill. Transactions of the Japan Society of Mechanical Engineers, Part C 62(601): 3691–3697CrossRefGoogle Scholar
  23. Mehrabadi I. M., Nouri M., Madoliat R. (2009) Investigating chatter vibration in deep drilling, including process damping and the gyroscopic effect. International Journal Of Machine Tools and Manufacture 49(12–13): 939–946CrossRefGoogle Scholar
  24. Michalewicz Z. (1996) Genetic algorithms + data structures = evolution programs. Springer, BerlinGoogle Scholar
  25. Nandi A. K., Davim J. P. (2009) A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatronics 19(2): 218–232CrossRefGoogle Scholar
  26. Sanjay C., Neema M. L., Chin C. W. (2005) Modeling of tool wear in drilling by statistical analysis and artificial neural network. Journal of Materials Processing Technology 170(3): 494–500CrossRefGoogle Scholar
  27. Schafer J. L. (1997) Analysis of incomplete multivariate data. Chapman & Hall/CRC, Boca RatonCrossRefGoogle Scholar
  28. Wei, W., & Tang, Y. (2003). A generic neural network approach for filling missing data in data mining. IEEE international conference on systems, man and cybernetics, 2003 pp. 862–867.Google Scholar
  29. Weinert K., Inasaki I., Sutherland J. W., Wakabayashi T. (2004) Dry machining and minimum quantity lubrication. Ann. CIRP. 53(2): 511–537CrossRefGoogle Scholar
  30. Zawistowski, P., & Grzenda, M. (2009). Handling incomplete data using evolution of imputation methods. Proceedings of 9th international conference ICANNGA 2009. Lecture notes in computer science (Vol. 5495, pp. 22–31). Berlin: Springer-Verlag.Google Scholar
  31. Zang J. Y., Liang S. Y., Yao J., Chen J. M., Hang J. L. (2006) Evolutionary optimization of machining processes. Journal of Intelligent Manufacturing 17(2): 203–215CrossRefGoogle Scholar
  32. Zhang J. Z., Chen J. C. (2009) Surface roughness optimization in a drilling operation using the taguchi design method. Materials And Manufacturing Processes 24(4): 459–467CrossRefGoogle Scholar

Copyright information

© The Author(s) 2010

Authors and Affiliations

  • Maciej Grzenda
    • 1
  • Andres Bustillo
    • 2
  • Pawel Zawistowski
    • 3
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  2. 2.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  3. 3.Faculty of Electronics and Information TechnologiesWarsaw University of TechnologyWarsawPoland

Personalised recommendations