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3. Teti, R., Jemielniak, K., O'Donnell, G., Dornfeld, D.. Advanced monitoring of machining operations. CIRP annals ... manufacturing technology, vol.59, no.2, 717-739.

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4. Ridwan, F., Xu, X.. Advanced CNC system with in-process feed-rate optimisation. Robotics and computer-integrated manufacturing, vol.29, no.3, 12-20.

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5. Tapoglou, Nikolaos, Mehnen, Jörn, Vlachou, Aikaterini, Doukas, Michael, Milas, Nikolaos, Mourtzis, Dimitris. Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring. Journal of manufacturing science and engineering, vol.137, no.4, 040909-.

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6. Li, Y., Liu, C., Hao, X., Gao, J.X., Maropoulos, P.G.. Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts. CIRP annals ... manufacturing technology, vol.64, no.1, 173-176.

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7. Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., Gostimirovic, M.. Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of intelligent manufacturing, vol.24, no.4, 755-762.

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8. Tapoglou, Nikolaos, Mehnen, Jörn, Vlachou, Aikaterini, Doukas, Michael, Milas, Nikolaos, Mourtzis, Dimitris. Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring. Journal of manufacturing science and engineering, vol.137, no.4, 040909-.

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9. Mourtzis, D., Vlachou, E., Xanthopoulos, N., Givehchi, M., Wang, L.. Cloud-based adaptive process planning considering availability and capabilities of machine tools. Journal of manufacturing systems, vol.39, 1-8.

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10. Lamraoui, Mourad, Barakat, Mustapha, Thomas, Marc, Badaoui, Mohamed El. Chatter detection in milling machines by neural network classification and feature selection. Journal of vibration and control : JVC, vol.21, no.7, 1251-1266.

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11. Bustillo, Andrés, Rodríguez, Juan J.. Online breakage detection of multitooth tools using classifier ensembles for imbalanced data. International journal of systems science, vol.45, no.12, 2590-2602.

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12. Wang, Jinjiang, Xie, Junyao, Zhao, Rui, Zhang, Laibin, Duan, Lixiang. Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and computer-integrated manufacturing, vol.45, 47-58.

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13. Wang, G.F., Yang, Y.W., Zhang, Y.C., Xie, Q.L.. Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection. Sensors and actuators. A, Physical, vol.209, 24-32.

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14. Wang, G., Yang, Y., Xie, Q., Zhang, Y.. Force based tool wear monitoring system for milling process based on relevance vector machine. Advances in engineering software, vol.71, 46-51.

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15. Wang, Guofeng, Qian, Lei, Guo, Zhiwei. Continuous tool wear prediction based on Gaussian mixture regression model. International journal of advanced manufacturing technology, vol.66, no.9, 1921-1929.

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16. Nouri, Mehdi, Fussell, Barry K., Ziniti, Beth L., Linder, Ernst. Real-time tool wear monitoring in milling using a cutting condition independent method. International journal of machine tools & manufacture, vol.89, 1-13.

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17. Liu, Tien-I, Song, Shin-Da, Liu, George, Wu, Zhang. Online monitoring and measurements of tool wear for precision turning of stainless steel parts. International journal of advanced manufacturing technology, vol.65, no.9, 1397-1407.

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18. Wang, G., Guo, Z., Yang, Y.. Force sensor based online tool wear monitoring using distributed Gaussian ARTMAP network. Sensors and actuators. A, Physical, vol.192, 111-118.

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19. Karandikar, Jaydeep, McLeay, Tom, Turner, Sam, Schmitz, Tony. Tool wear monitoring using naïve Bayes classifiers. International journal of advanced manufacturing technology, vol.77, no.9, 1613-1626.

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20. Wang, Guofeng, Yang, Yinwei, Li, Zhimeng. Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model. Sensors, vol.14, no.11, 21588-21602.

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21. Ritou, M., Garnier, S., Furet, B., Hascoet, J.Y.. Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mechanical systems and signal processing, vol.44, no.1, 211-220.

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22. Antić, A., Šimunović, G., Šarić, T., Milošević, M., and Ficko, M., “A Model of Tool Wear Monitoring System for Turning,” Tehnicki Vjesnik/Technical Gazette, Vol. 20, No. 2, pp. 247-254, 2013. 

23. Aghdam, B. H., Vahdati, M., Sadeghi, M. H.. Vibration-based estimation of tool major flank wear in a turning process using ARMA models. International journal of advanced manufacturing technology, vol.76, no.9, 1631-1642.

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24. Li, Weilin, Fu, Pan, Cao, Weiqing. Study on Feature Selection and Identification Method of Tool Wear States Based on Svm. International journal on smart sensing and intelligent systems, vol.6, no.2, 448-465.

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25. Maia, L.H.A., Abrao, A.M., Vasconcelos, W.L., Sales, W.F., Machado, A.R.. A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission. Tribology international, vol.92, 519-532.

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26. Downey, Jonathan, O׳Leary, Paul, Raghavendra, Ramesh. Comparison and analysis of audible sound energy emissions during single point machining of HSTS with PVD TiCN cutter insert across full tool life. Wear: An international journal on the science and technology of friction, lubrication and wear, vol.313, no.1, 53-62.

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27. Hung, Chien-Wei, Lu, Ming-Chyuan. Model development for tool wear effect on AE signal generation in micromilling. International journal of advanced manufacturing technology, vol.66, no.9, 1845-1858.

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28. Bhat, Nagaraj N., Dutta, Samik, Vashisth, Tarun, Pal, Srikanta, Pal, Surjya K., Sen, Ranjan. Tool condition monitoring by SVM classification of machined surface images in turning. International journal of advanced manufacturing technology, vol.83, no.9, 1487-1502.

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29. Čerče, Luka, Pušavec, Franci, Kopač, Janez. A New Approach to Spatial Tool Wear Analysis and Monitoring. Strojniśki vestnik, vol.61, no.9, 489-497.

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30. Stavropoulos, P., Papacharalampopoulos, A., Vasiliadis, E., Chryssolouris, G.. Tool wear predictability estimation in milling based on multi-sensorial data. International journal of advanced manufacturing technology, vol.82, no.1, 509-521.

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31. Tonissen, S., Koike, R., Kakinuma, Y., Aoyama, T., Klocke, F.. Monitoring of tool collision in drilling by disturbance observer. CIRP Journal of manufacturing science and technology, vol.7, no.3, 274-282.

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32. Drouillet, C., Karandikar, J., Nath, C., Journeaux, A.C., El Mansori, M., Kurfess, T.. Tool life predictions in milling using spindle power with the neural network technique. Journal of manufacturing processes, vol.22, 161-168.

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33. Cao, H., Lei, Y., He, Z.. Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. International journal of machine tools & manufacture, vol.69, 11-19.

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34. Huang, Panling, Li, Jianfeng, Sun, Jie, Zhou, Jun. Vibration analysis in milling titanium alloy based on signal processing of cutting force. International journal of advanced manufacturing technology, vol.64, no.5, 613-621.

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35. Cao, H., Zhou, K., Chen, X.. Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. International journal of machine tools & manufacture, vol.92, 52-59.

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36. Lamraoui, M., Thomas, M., El Badaoui, M., Girardin, F.. Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mechanical systems and signal processing, vol.44, no.1, 72-85.

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37. Grossi, N., Scippa, A., Sallese, L., Sato, R., Campatelli, G.. Spindle speed ramp-up test: A novel experimental approach for chatter stability detection. International journal of machine tools & manufacture, vol.89, 221-230.

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38. Galdino dos Santos, Raphael, Teixeira Coelho, Reginaldo. A Contribution to Improve the Accuracy of Chatter Prediction in Machine Tools Using the Stability Lobe Diagram. Journal of manufacturing science and engineering, vol.136, no.2, 021005-.

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39. Frumusanu, Gabriel R., Constantin, Ionut C., Marinescu, Vasile, Epureanu, Alexandru. Development of a stability intelligent control system for turning. International journal of advanced manufacturing technology, vol.64, no.5, 643-657.

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40. Moradi, H., Vossoughi, G., Movahhedy, M.R., Salarieh, H.. Suppression of nonlinear regenerative chatter in milling process via robust optimal control. Journal of process control, vol.23, no.5, 631-648.

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41. Ma, Lei, Melkote, Shreyes N., Castle, James B.. A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling. Journal of manufacturing science and engineering, vol.135, no.3, 031007-.

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42. Filippov, A.V., Nikonov, A.Y., Rubtsov, V.E., Dmitriev, A.I., Tarasov, S.Yu.. Vibration and acoustic emission monitoring the stability of peakless tool turning: Experiment and modeling. Journal of materials processing technology, vol.246, 224-234.

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44. Balasundaram, Mohan Kumar, Ratnam, Mani Maran. In-Process Measurement of Surface Roughness using Machine Vision with Sub-Pixel Edge Detection in Finish Turning. International journal of precision engineering and manufacturing, vol.15, no.11, 2239-2249.

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45. Albertelli, P., Goletti, M., Torta, M., Salehi, M., Monno, M.. Model-based broadband estimation of cutting forces and tool vibration in milling through in-process indirect multiple-sensors measurements. International journal of advanced manufacturing technology, vol.82, no.5, 779-796.

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46. Krüger, M., Denkena, B.. A model-based approach for monitoring of shape deviations in peripheral milling. International journal of advanced manufacturing technology, vol.67, no.9, 2537-2550.

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47. Yashiro, T., Ogawa, T., Sasahara, H.. Temperature measurement of cutting tool and machined surface layer in milling of CFRP. International journal of machine tools & manufacture, vol.70, 63-69.

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48. Liu, J., Chen, G., Ji, C., Qin, X., Li, H., Ren, C.. An investigation of workpiece temperature variation of helical milling for carbon fiber reinforced plastics (CFRP). International journal of machine tools & manufacture, vol.86, 89-103.

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49. Feng, Jilu, Sun, Zhili, Jiang, Zenghui, Yang, Li. Identification of chatter in milling of Ti-6Al-4V titanium alloy thin-walled workpieces based on cutting force signals and surface topography. International journal of advanced manufacturing technology, vol.82, no.9, 1909-1920.

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51. Hase, Alan, Wada, Masaki, Koga, Toshihiko, Mishina, Hiroshi. The relationship between acoustic emission signals and cutting phenomena in turning process. International journal of advanced manufacturing technology, vol.70, no.5, 947-955.

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52. Segreto, T., Simeone, A., Teti, R.. Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning. CIRP Journal of manufacturing science and technology, vol.7, no.3, 202-209.

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53. O'Driscoll, E., Kelly, K., O'Donnell, G.E.. Intelligent energy based status identification as a platform for improvement of machine tool efficiency and effectiveness. Journal of cleaner production, vol.105, 184-195.

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54. Lu, Lei, Yan, Jihong, de Silva, Clarence W.. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. Journal of sound and vibration, vol.344, 464-483.

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55. Yamada, Yuki, Kakinuma, Yasuhiro. Sensorless cutting force estimation for full-closed controlled ball-screw-driven stage. International journal of advanced manufacturing technology, vol.87, no.9, 3337-3348.

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