IJAT Vol.12 No.3 pp. 275-281
doi: 10.20965/ijat.2018.p0275


Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling

Alessandra Caggiano*,**,† and Luigi Nele***

*Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT UniNaples)
P.le Tecchio 80, Naples 80125, Italy

**Department of Industrial Engineering, University of Naples Federico II, Naples, Italy

Corresponding author

***Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy

September 30, 2017
November 24, 2017
Online released:
May 1, 2018
May 5, 2018
drilling, composite material, sensor monitoring, artificial neural network, tool wear prediction

An intelligent sensor monitoring procedure was implemented to monitor the drilling of carbon fiber reinforced plastic (CFRP)/CFRP stacks used in the assembly of aircraft fuselage panels; the signals from these sensors were then used to develop an artificial neural network-based cognitive paradigm to predict tool wear, which would allow on-line decision making regarding tool replacement. A multiple sensor system, capable of acquiring signals relative to thrust force, torque, and acoustic emission RMS, was employed during experimental drilling tests, under different rotational speed and feed conditions. Advanced sensor signal processing techniques, including signal conditioning and segmentation, as well as statistical feature extraction and data fusion, were implemented on the acquired signals. Selected statistical features extracted from the multiple sensor signals in the time domain were combined via sensor fusion techniques to construct sensor fusion pattern vectors. These were then fed to artificial neural networks for pattern recognition, with the goal of finding correlations which would allow the prediction of the corresponding tool wear. The tool wear prediction performed by the artificial neural network can be utilized to support decision making at the appropriate time for worn tool replacement, which is extremely useful for drilling automation, as well as for estimating the quality of the drilled holes.

Cite this article as:
A. Caggiano and L. Nele, “Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling,” Int. J. Automation Technol., Vol.12, No.3, pp. 275-281, 2018.
Data files:
  1. [1] R. Teti, “Machining of Composite Materials,” CIRP Annals, Vol.51, No.2, pp. 611-634, 2002.
  2. [2] A. Kitano, “Characteristics of Carbon-Fiber-reinforced plastics (CFRP) and associated challenges – Focusing on Carbon-Fiber-reinforced thermosetting resins (CFRTS) for aircraft,” Int. J. of Automation Technology, Vol.10, No.3, pp. 300-309, 2016.
  3. [3] R. M’Saoubi, D. Axinte, S. L. Soo, C. Nobel, H. Attia, G. Kappmeyer, S. Engin, and W.-M. Sim, “High performance cutting of advanced aerospace alloys and composite materials,” CIRP Annals, Vol.64, No.2, pp. 557-580, 2015.
  4. [4] V. Lopresto, A. Caggiano, and R. Teti, “High Performance Cutting of Fibre Reinforced Plastic Composite Materials,” Procedia CIRP, Vol.46, pp. 71-82, 2016.
  5. [5] T. Inoue and M. Hagino, “Cutting Characteristics of CFRP Materials with Carbon Fiber Distribution,” Int. J. of Automation Technology, Vol.7, No.3, pp. 285-291, 2013.
  6. [6] T. Inoue and M. Hagino, “Effect of Carbon Fiber Orientation and Helix Angle on CFRP Cutting Characteristics by End-Milling,” Int. J. of Automation Technology, Vol.7, No.3, pp. 292-299, 2013.
  7. [7] S. Sakamoto, “Precision Drilling of Carbon Fiber Reinforced Plastics with Ball Nose End Mills,” Int. J. of Automation Technology, Vol.10, No.3, pp. 334-340, 2016.
  8. [8] M. Sato, H. Tanaka, and K. Yamamoto, “Temperature Variations in Drilling of CFRP/Aluminum and CFRP/Titanium Stacks,” Int. J. of Automation Technology, Vol.10, No.3, pp. 348-355, 2016.
  9. [9] Y. Karpat, O. Bahtiyar, B. Değer, and B. Kaftanoğlu, “A mechanistic approach to investigate drilling of UD-CFRP laminates with PCD drills,” CIRP Annals, Vol.63, No.1, pp. 81-4, 2014.
  10. [10] A. Sadek, B. Shi, M. Meshreki, J. Duquesne, and M. H. Attia, “Prediction and control of drilling-induced damage in fibre-reinforced polymers using a new hybrid force and temperature modelling approach,” CIRP Annals, Vol.64, No.1, pp. 89-92, 2015.
  11. [11] A. Sadek, M. H. Attia, M. Meshreki, and B. Shi, “Characterization and optimization of vibration-assisted drilling of fibre reinforced epoxy laminates,” CIRP Annals, Vol.62, No.1, pp. 91-94, 2013.
  12. [12] A. Sadek, M. Meshreki, and M. H. Attia, “Characterization and optimization of orbital drilling of woven carbon fiber reinforced epoxy laminates,” CIRP Annals, Vol.61, No.1, pp. 123-126, 2012.
  13. [13] C. C. Tsao, H. Hocheng, and Y. C. Chen “Delamination reduction in drilling composite materials by active backup force,” CIRP Annals, Vol.61, No.1, pp. 91-94, 2012.
  14. [14] S. Rawat and H. Attia, “Characterization of the dry high speed drilling process of woven composites using Machinability Maps approach,” CIRP Annals, Vol.58, No.1, pp. 105-108, 2009.
  15. [15] S. Tamura and T. Matsumura, “Cutting Force Prediction in Drilling of Unidirectional Carbon Fiber Reinforced Plastics,” Int. J. of Automation Technology, Vol.9, No.1, pp. 59-66, 2015.
  16. [16] R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Annals – Manufacturing Technology, Vol.59, No.2, pp. 717-739, 2010.
  17. [17] R. Teti, “Advanced IT methods of signal processing and decision making for zero defect manufacturing in machining,” Procedia CIRP, Vol.28, pp. 3-15, 2015.
  18. [18] T. Segreto, A. Caggiano, and R. Teti, “Neuro-fuzzy System Implementation in Multiple Sensor Monitoring for Ni-Ti Alloy Machinability Evaluation,” CIRPe 2015 – Understanding the life cycle implications of manufacturing, Procedia CIRP, Vol.37, pp. 193-198, 2015.
  19. [19] R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enabled prognosis for manufacturing,” CIRP Annals, Vol.64/2, pp. 749-772, 2015.
  20. [20] S. Dolinšek, B. Šuštaršič, and J. Kopač, “Wear mechanisms of cutting tools in high-speed cutting processes,” Wear, Vol.250, No.1-12, pp. 349-356, 2001.
  21. [21] C. Bonnet, G. Poulachon, J. Rech, Y. Girard, and J. P. Costes, “CFRP drilling: Fundamental study of local feed force and consequences on hole exit damage,” Int. J. of Machine Tools and Manufacture, Vol.94, pp. 57-64, 2015.
  22. [22] A. Caggiano, R. Angelone, and R. Teti, “Image Analysis for CFRP Drilled Hole Quality Assessment,” Procedia CIRP, Vol.62, pp. 440-445, 2017.
  23. [23] C. M. Bishop, “Neural networks for pattern recognition,” Clarendon Press, 1995.
  24. [24] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern classification,” 2nd ed., Wiley, 2001.
  25. [25] R. Jang, C. T. Sun, and E. Mizutani, “Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence,” Prentice Hall, 1997.
  26. [26] S. Abe, “Pattern classification: neuro-fuzzy methods and their comparison,” Springer-Verlag London, 2001.

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Last updated on Aug. 16, 2018