Mechanics & Industry
Volume 20, Number 8, 2019
Selected scientific topics in recent applied engineering – 20 Years of the ‘French Association of Mechanics – AFM’
Article Number 809
Number of page(s) 9
Published online 21 July 2020
  1. A. Beskri, H. Mejri, K. Mehdi, J.F. Rigal, Systèmes de surveillance automatique en usinage: Moyens et méthodes [Automatic monitoring systems in machining: tools and methods], in French Mechanics Congress, 2013 [Google Scholar]
  2. IFPM-Formation, Usinage: Tournage Fraisage (IFPM courses: Machining: Milling Turning), September 2015 [Google Scholar]
  3. T. Mikołajczyk, K. Nowicki, A. Bustillo, D. Pimenov, Predicting tool life in turning operations using neural networks and image processing, Mech. Syst. Signal Process. 104, 503–513 (2018) [Google Scholar]
  4. D. Pimenov, A. Bustillo, T. Mikołajczyk, Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth, J. Intell. Manufactur. 29, 1045–1061 (2018) [CrossRef] [Google Scholar]
  5. M. Correa, C. Bielza, J. Pamies-Teixeira, Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process, Int. J. Expert Syst. Appl. 36, 7270–7279 (2009) [CrossRef] [Google Scholar]
  6. A.I.H. Committee, ASM Handbook Volume 16: Machining, ASM International, 1989. [Google Scholar]
  7. J. Kindinger, Lightweight structural cores, ASM Handbook Met. Composites 21 (2001) [Google Scholar]
  8. D. Gay, Matériaux composites [Composite materials], Hermes, 2015 [Google Scholar]
  9. R. Carl, Three-dimensional honeycomb core machining apparatus and method, US Pat. App. 13/707, 670.1, 2012 [Google Scholar]
  10. M. Jaafar, S. Atlati, H. Makich, M. Nouari, A. Moufki, B. Julliere, A 3D FE modeling of machining process of Nomex® honeycomb core: influence of the cell structure behaviour and specific tool geometry, Proc. CIRP 58, 505–510 (2017) [CrossRef] [Google Scholar]
  11. M. Mendoza et al., Development of a new milling cutter for aluminum honeycomb, Int. J. Mach. Tool Des. Res. 23, 81–91 (1983) [CrossRef] [Google Scholar]
  12. H. Tchoutouo, Adhesiveless honeycomb sandwich structure with carbon graphite prepreg for primary structural application: a comparative study to the use of adhesive film, Master thesis, Wichita State University, May 2012 [Google Scholar]
  13. J. Rion, Y. Leterrier, E. Manson, Prediction of the adhesive fillet size for skin to honeycomb core bonding in ultra-light sandwich structures, Compos. Part A 39, 1547–1555 (2008) [CrossRef] [Google Scholar]
  14. P. Agusmian, T. Ooijevaar, B. Kilundu, Automated bearing fault diagnostics with cost effective vibration sensor, WCEAM/VETOMAC, Conference, 2017 [Google Scholar]
  15. C.K. Madhusudana, S. Budati, N. Gangadhar, H. Kumar, S. Narendranath, Fault diagnosis studies of face milling cutter using machine learning approach, J. Low Freq. Noise Vib. Active Control 35, 128–138 (2016) [CrossRef] [Google Scholar]
  16. S. Gopal, K. Kishore, Normalization: A Preprocessing stage, CSE & IT department, VSSUT, Burla, India, 2015 [Google Scholar]
  17. The MathWorks Inc., “Mastering Machine Learning A Step-by-Step Guide with MATLAB”, Matlab Ebook section 1 to 4, ©2018 [Google Scholar]
  18. R. Vidal, Y. Ma, S.S. Sastry, Generalized Principal Component Analysis, Springer, 2016 [CrossRef] [Google Scholar]
  19. F. Yang, Z. Yun, Q. Haiyu, L. Dequn, Z. Huamin, L. Jürgen, Analysis of feature extracting ability for cutting state monitoring using deep belief networks, Conf. Model. Mach. Oper. 31, 29–34 (2015) [Google Scholar]
  20. G. Chen, X. Wei, R. Yan, Z. Yuqing, Numerical control machine tool fault diagnosis using hybrid stationary subspace analysis and least squares support vector machine with a single sensor, in MDPI Conf. 7, 346–358 (2017) [Google Scholar]
  21. C. Zhang, X. Yao, J. Zhang, H. Jin, Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations, MDPI Conf. 16, 795–815 (2016) [Google Scholar]
  22. M. Kubat, An introduction to machine learning, 2nd edn., Springer, 2017 [CrossRef] [Google Scholar]
  23. S. Shalev-Shwartz, S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014 [CrossRef] [Google Scholar]
  24. Z. Rui, W. Dongzhe, Y. Ruqiang, M. Kezhi, S. Fei, J. Wang, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Ind. Electr. 65, 1539–1548 (2018) [CrossRef] [Google Scholar]
  25. D. Wu, C. Jennings, J. Terpenny, S. Kumara, Cloud-based machine learning for predictive analytics: tool wear prediction in milling, IEEE Int. Conf. Big Data 2062–2069 (2016) [Google Scholar]
  26. K. Javed, R. Gouriveau, N. Zerhounic, P. Nectoux, Enabling health monitoring approach based on vibration data for accurate prognostics, IEEE Trans. Ind. Electr. 62, 647–656 (2015) [CrossRef] [Google Scholar]
  27. D. Karayel, Prediction and control of surface roughness in CNC lathe using artificial neural network, J. Mater. Process. Technol. 209, 3125–3137 (2009) [CrossRef] [Google Scholar]
  28. A. Mohd Zain, H. Haron, S. Sharif, Prediction of surface roughness in the end milling machining using Artificial Neural Network, Exp. Syst. Appl. 37, 1755–1768 (2010) [CrossRef] [Google Scholar]
  29. L. Codjo, M. Jaafar, H. Makich, D. Knittel, M. Nouari, Milling diagnosis using machine learning techniques toward Industry 4.0, in 29th International Workshop on Principles of Diagnosis DX'18, Warsaw, August 2018 [Google Scholar]
  30. R. Myers, D. Montgomery, C. Anderson-Cook, Response surface methodology, Wiley, 2016 [Google Scholar]
  31. D. Yu Pimenov, A. Bustillo, T. Mikolajczyk, Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth, J. Intell. Manufactur. 29, 1045–1061 (2018) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.