Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Process control based on pattern recognition for routing carbon fiber reinforced polymer

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Carbon fiber reinforced polymer (CFRP) is an important composite material. It has many applications in aerospace and automotive fields. The little information available about the machining process of this material, specifically when routing process is considered, makes the process control quite difficult. In this paper, we propose a new process control technique and we apply it to the routing process for that important material. The measured machining conditions are used to evaluate the quality and the geometric profile of the machined part. The machining conditions, whether controllable or uncontrollable are used to control part accuracy and its quality. We present a pattern-based machine learning approach in order to detect the characteristic patterns, and use them to control the quality of a machined part at specific range. The approach is called logical analysis of data (LAD). LAD finds the characteristic patterns which lead to conforming products and those that lead to nonconforming products. As an example, LAD is used for online control of a simulated routing process of CFRP. We introduce the LAD technique, we apply it to the high speed routing of woven carbon fiber reinforced epoxy, and we compare the accuracy of LAD to that of an artificial neural network, since the latter is the most known machine learning technique. By using experimental results, we show how LAD is used to control the routing process by tuning autonomously the routing conditions. We conclude with a discussion of the potential use of LAD in manufacturing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Benardos, P., & Vosniakos, G. (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics and Computer-Integrated Manufacturing, 18(5), 343–354.

  • Bennane, A., & Yacout, S. (2012). LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance. Journal of Intelligent Manufacturing, 23(2), 265–275.

    Article  Google Scholar 

  • Bores, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation of logical analysis of data. IEEE Transactions on Knowledge and Data Engineering, 12(2), 292–306.

    Article  Google Scholar 

  • Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650.

    Article  Google Scholar 

  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2011). SMOTE: Synthetic minority over-sampling technique. arXiv preprint arXiv:1106.1813.

  • Coker, S. A., & Shin, Y. C. (1996). In-process control of surface roughness due to tool wear using a new ultrasonic system. International Journal of Machine Tools and Manufacture, 36(3), 411–422.

    Article  Google Scholar 

  • Davim, J. P., & Reis, P. (2005). Damage and dimensional precision on milling carbon fiber-reinforced plastics using design experiments. Journal of Materials Processing Technology, 160(2), 160–167.

    Article  Google Scholar 

  • Du, S., Lv, J., & Xi, L. (2012). A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge. Journal of Intelligent Manufacturing, 23(5), 1833–1847.

    Article  Google Scholar 

  • Elliott, C., Vijayakumar, V., Zink, W., & Hansen, R. (2007). National instruments LabVIEW: A programming environment for laboratory automation and measurement. Journal of the Association for Laboratory Automation, 12(1), 17–24.

    Article  Google Scholar 

  • Ferreira, J., Coppini, N., & Miranda, G. (1999). Machining optimisation in carbon fibre reinforced composite materials. Journal of Materials Processing Technology, 92, 135–140.

    Article  Google Scholar 

  • Haber, R. E., Haber, R., Alique, A., & Ros, S. (2002). Application of knowledge-based systems for supervision and control of machining processes. Handbook of Software Engineering and Knowledge Engineering, 2, 327–362.

    Google Scholar 

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.

    Article  Google Scholar 

  • Hammer, P. L. (1986). Partially defined Boolean functions and cause-effect relationships. In International conference on multi-attribute decision making via or-based expert systems.

  • Hammer, P. L., & Bonates, T. O. (2006). Logical analysis of data—an overview: From combinatorial optimization to medical applications. Annals of Operations Research, 148(1), 203–225.

    Article  Google Scholar 

  • Hansen, P., & Meyer, C. (2011). A new column generation algorithm for logical analysis of data. Annals of Operations Research, 188(1), 215–249.

    Article  Google Scholar 

  • Huang, P. B. (2014). An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0907-6.

  • Landers, R. G., Ulsoy, A. G., & Furness, R. J. (2002). Process monitoring and control of machining operations. In The mechanical systems design handbook.

  • Liang, S. Y., Hecker, R. L., & Landers, R. G. (2004). Machining process monitoring and control: The state-of-the-art. Journal of Manufacturing Science and Engineering, 126(2), 297–310.

    Article  Google Scholar 

  • Linderoth, J. T., & Lodi, A. (2011). MILP software. In Wiley encyclopedia of operations research and management science.

  • Mayoraz, E., & Moreira, M. (1999). Combinatorial approach for data binarization. In Principles of data mining and knowledge discovery (pp. 442–447). Springer, Berlin.

  • Meshreki, M., Sadek, A., & Attia, M. H. (2012). High speed routing of woven carbon fiber reinforced epoxy laminates. In Proceedings of the ASME 2012 international mechanical engineering congress & exposition, Houston, Texas, USA.

  • Mortada, M.-A., Carroll Iii, T., Yacout, S., & Lakis, A. (2009). Rogue components: Their effect and control using logical analysis of data. Journal of Intelligent Manufacturing, 23(2), 289–302.

  • Mortada, M.-A., Yacout, S., & Lakis, A. (2011). Diagnosis of rotor bearings using logical analysis of data. Journal of Quality in Maintenance Engineering, 17(4), 371–397. doi:10.1108/13552511111180186.

    Article  Google Scholar 

  • Mortada, M.-A., Yacout, S., & Lakis, A. (2013). Fault diagnosis in power transformers using multi-class logical analysis of data. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0750-1.

  • Rahman, M., Ramakrishna, S., Prakash, J., & Tan, D. (1999). Machinability study of carbon fiber reinforced composite. Journal of Materials Processing Technology, 89, 292–297.

    Article  Google Scholar 

  • Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (1995). Artificial intelligence: A modern approach (vol. 74). Prentice Hall, Englewood Cliffs.

  • Ryoo, H. S., & Jang, I. Y. (2009). Milp approach to pattern generation in logical analysis of data. Discrete Applied Mathematics, 157(4), 749–761.

    Article  Google Scholar 

  • Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473–483.

    Article  Google Scholar 

  • Software, C. (2012). Patent Cooperation Treaty PCT/CA2011/000876, No. Wo 2012/00984.

  • Teti, R. (2002). Machining of composite materials. CIRP Annals-Manufacturing Technology, 51(2), 611–634.

    Article  Google Scholar 

  • Wang, H., & Huang, Q. (2006). Error cancellation modeling and its application to machining process control. IIE Transactions, 38(4), 355–364.

    Article  Google Scholar 

  • Witten, I. H., & Frank, E. (2011). Data mining: Practical machine learning tools and techniques. Los Altos: Morgan Kaufmann.

    Google Scholar 

  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Los Altos: Morgan Kaufmann.

    Google Scholar 

  • Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341–1390.

    Article  Google Scholar 

  • Yacout, S. (2010). Fault detection and diagnosis for condition based maintenance using the logical analysis of data. In 40th International conference on computers and industrial engineering, Japan. IEEE Computer Society. doi:10.1109/iccie.2010.5668357.

  • Zhang, J. Z., Chen, J. C., & Kirby, E. D. (2007). The development of an in-process surface roughness adaptive control system in turning operations. Journal of Intelligent Manufacturing, 18(3), 301–311.

    Article  Google Scholar 

  • Zuperl, U., Cus, F., & Reibenschuh, M. (2012). Modeling and adaptive force control of milling by using artificial techniques. Journal of Intelligent Manufacturing, 23(5), 1805–1815.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Industrial Engineering, École Polytechnique, C.P. 6079, Succ. Centre-Ville, Montréal, QC, H3C3A7, Canada

    Yasser Shaban & Soumaya Yacout

  2. Aerospace Manufacturing Technology Centre, National Research Council Canada, 5145 Decelles Avenue, Montréal, QC, H3T2B2, Canada

    Mouhab Meshreki & Helmi Attia

  3. Department of Mechanical Engineering, École Polytechnique, C.P. 6079, Succ. Centre-Ville, Montréal, QC, H3C3A7, Canada

    Marek Balazinski

Authors
  1. Yasser Shaban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mouhab Meshreki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soumaya Yacout
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marek Balazinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Helmi Attia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumaya Yacout.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaban, Y., Meshreki, M., Yacout, S. et al. Process control based on pattern recognition for routing carbon fiber reinforced polymer. J Intell Manuf 28, 165–179 (2017). https://doi.org/10.1007/s10845-014-0968-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0968-6

Keywords

Search

Navigation