X. Jin, B. W. Wah, X. Cheng, and Y. Wang, Significance and Challenges of Big Data Research, Big Data Research, vol.2, issue.2, pp.59-64, 2015.
DOI : 10.1016/j.bdr.2015.01.006

A. K. Choudhary, J. A. Harding, and M. K. Tiwari, Data mining in manufacturing: a review based on the kind of knowledge, Journal of Intelligent Manufacturing, vol.24, issue.3, pp.501-521, 2009.
DOI : 10.1016/S0956-053X(00)00045-3

C. Brecher, G. Quintana, and T. Rudolf, Use of NC kernel data for surface roughness monitoring in milling operations, The International Journal of Advanced Manufacturing Technology, vol.22, issue.3, pp.953-962, 2011.
DOI : 10.1080/09511920802287138

C. De-castelbajac, M. Ritou, S. Laporte, and B. Furet, Monitoring of distributed defects on HSM spindle bearings, Applied Acoustics, vol.77, pp.159-168, 2014.
DOI : 10.1016/j.apacoust.2013.07.008
URL : https://hal.archives-ouvertes.fr/hal-00852246

A. Vijayaraghavan, L. Huet, D. Dornfeld, W. Sobel, B. Blomquist et al., Process Planning and Verification with MTConnect, Transactions of NAMRI/SMI 37, 2009.

, OPC unified architecture -Part 1: Overview and concepts, International Electrotechnical Commission, 2016.

J. C. Chen, An effective fuzzy-nets training scheme for monitoring tool breakage, Journal of Intelligent Manufacturing, vol.11, issue.1, pp.85-101, 2000.
DOI : 10.1023/A:1008908309585

R. Teti, K. Jemielniak, G. O-'donnell, and D. Dornfeld, Advanced monitoring of machining operations, CIRP Annals, vol.59, issue.2, pp.717-739, 2010.
DOI : 10.1016/j.cirp.2010.05.010

J. V. Abellan-nebot and F. Romero-subirón, A review of machining monitoring systems based on artificial intelligence process models, The International Journal of Advanced Manufacturing Technology, vol.45, issue.158, pp.1-21, 2010.
DOI : 10.1016/j.matdes.2005.07.004

C. H. Lauro, L. C. Brandão, D. Baldo, R. A. Reis, J. P. Davim et al., Monitoring and processing signal applied in machining processes?a review Advance in chatter detection in ball end milling process by utilizing wavelet transform, Measurement Journal of Intelligent Manufacturing, vol.58, issue.263, pp.73-86, 2014.

B. Chabane, S. Stoica-maniu, C. Camacho, E. Alamo, T. Dumur et al., Fault Detection using Set-Membership estimation based on Multiple Model systems Angular approach combined to mechanical model for tool breakage detection by eddy current sensors, Mechanical Systems and Signal Processing Surface roughness monitoring application based on artificial neural networks for ball-end milling operations Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method Vibration sensor based tool condition monitoring using support vector machine and locality preserving projection, Sensors and Actuators, A : Physical, pp.1105-1110, 2011.

M. Lamraoui, M. Thomas, M. El-badaoui-eleftheriadis, R. , J. Myklebust et al., Benchmark and Best Practice of IFaCOM Industrial Demonstrators Selfoptimizing Cutting Process Using Learning Process Models A cyber-physical systems architecture for industry 4.0-based manufacturing systems, 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Procedia CIRP Graphical models in applied multivariate statistics Influence of spindle condition on the dynamic behaviour, Annals of the CIRP, pp.177-198, 2009.