Abellan-Nebot, J. V., & Romero Subirón, F. (2010). A review of machining monitoring systems based on artificial intelligence process models. International Journal of Advanced Manufacturing Technology, 47(1–4), 237–257. https://doi.org/10.1504/IJMMM.2010.034486.
Article
Google Scholar
Badar, M. A., Raman, S., & Pulat, P. S. (2005). Experimental verification of manufacturing error pattern and its utilization in form tolerance sampling. International Journal of Machine Tools and Manufacture, 45(1), 63–73. https://doi.org/10.1016/j.ijmachtools.2004.06.017.
Article
Google Scholar
Benardos, P. G., & Vosniakos, G.-C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844. https://doi.org/10.1016/S0890-6955(03)00059-2.
Article
Google Scholar
Bhattacharyya, P., Sengupta, D., Mukhopadhyay, S., & Chattopadhyay, A. B. (2008). On-line tool condition monitoring in face milling using current and power signals. International Journal of Production Research, 46(4), 1187–1201. https://doi.org/10.1080/00207540600940288.
Article
Google Scholar
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.
Article
Google Scholar
Bustillo, A., & Correa, M. (2012). Using artificial intelligence to predict surface roughness in deep drilling of steel components. Journal of Intelligent Manufacturing, 23(5), 1893–1902. https://doi.org/10.1007/s10845-011-0506-8.
Article
Google Scholar
Bustillo, A., Díez-Pastor, J.-F., Quintana, G., & García-Osorio, C. (2011). Avoiding neural network fine tuning by using ensemble learning: Application to ball-end milling operations. International Journal of Advanced Manufacturing Technology, 57(5–8), 521–532. https://doi.org/10.1007/s00170-011-3300-z.
Article
Google Scholar
Bustillo, A., Grzenda, M., & Macukow, B. (2016). Interpreting tree-based prediction models and their data in machining processes. Integrated Computer-Aided Engineering, 23(4), 349–367. https://doi.org/10.3233/ICA-160513.
Article
Google Scholar
Bustillo, A., Pimenov, D Yu, Matuszewski, M., & Mikolajczyk, T. (2018). Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels. Robotics and Computer Integrated Manufacturing, 53, 215–227. https://doi.org/10.1016/j.rcim.2018.03.011.
Article
Google Scholar
Bustillo, A., & Rodriguez, J. J. (2014). Online breakage detection of multitooth tools using classifier ensembles for imbalanced data. International Journal of Systems Science, 45(12), 2590–2602. https://doi.org/10.1080/00207721.2013.775378.
Article
Google Scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Article
Google Scholar
D’Addona, D. M., Ullah, A. M. M. S., & Matarazzo, D. (2017). Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. Journal of Intelligent Manufacturing, 28(6), 1285–1301. https://doi.org/10.1007/s10845-015-1155-0.
Article
Google Scholar
da Silva, R. H. L., da Silva, M. B., & Hassui, A. (2016). A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals. Machining Science and Technology, 20(3), 386–405. https://doi.org/10.1080/10910344.2016.1191026.
Article
Google Scholar
Davoudinejad, A., Annoni, M., Rebaioli, L., & Semeraro, Q. (2014) Improvement of surface flatness in high precision milling. In Conference proceedings—14th international conference of the European Society for precision engineering and nanotechnology, EUSPEN 2014 (Vol. 2, pp. 190–193).
Denkena, B., & Hasselberg, E. (2015). Influence of the cutting tool compliance on the workpiece surface shape in face milling of workpiece compounds. Procedia CIRP, 31, 7–12. https://doi.org/10.1016/j.procir.2015.03.074.
Article
Google Scholar
Dobrzynski, M., Chuchala, D., & Orlowski, K. A. (2018). The effect of alternative cutter paths on flatness deviations in the face milling of aluminum plate parts. Journal of Machine Engineering, 18(1), 80–87. https://doi.org/10.5604/01.3001.0010.8825.
Article
Google Scholar
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010.
Article
Google Scholar
Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research, 61, 863–905. https://doi.org/10.1613/jair.1.11192.
Article
Google Scholar
García-Ordás, M. T., Alegre, E., González-Castro, V., & Alaiz-Rodríguez, R. (2017). A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. International Journal of Advanced Manufacturing Technology, 90(5–8), 1947–1961. https://doi.org/10.1007/s00170-016-9541-0.
Article
Google Scholar
García-Ordás, M. T., Alegre-Gutiérrez, E., Alaiz-Rodríguez, R., & González-Castro, V. (2018). Tool wear monitoring using an online, automatic and low cost system based on local texture. Mechanical Systems and Signal Processing, 112, 98–112. https://doi.org/10.1016/j.ymssp.2018.04.035.
Article
Google Scholar
García-Pedrajas, N., Pérez-Rodríguez, J., García-Pedrajas, M., Ortiz-Boyer, D., & Fyfe, C. (2012). Class imbalance methods for translation initiation site recognition in DNA sequences. Knowledge-Based Systems, 25(1), 22–34. https://doi.org/10.1016/j.knosys.2011.05.002.
Article
Google Scholar
Grzenda, M., & Bustillo, A. (2019). Semi-supervised roughness prediction with partly unlabeled vibration data streams. Journal of Intelligent Manufacturing, 30(2), 933–945. https://doi.org/10.1007/s10845-018-1413-z.
Article
Google Scholar
Grzenda, M., Bustillo, A., Quintana, G., & Ciurana, J. (2012). Improvement of surface roughness models for face milling operations through dimensionality reduction. Integrated Computer-Aided Engineering, 19(2), 179–197. https://doi.org/10.3233/ICA-2012-0398.
Article
Google Scholar
Gu, F., Melkote, S. N., Kapoor, S. G., & Devor, R. E. (1997a). A model for the prediction of surface flatness in face milling. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 119(4 PART I), 476–484.
Article
Google Scholar
Gu, F., Melkote, S. N., Kapoor, S. G., & DeVor, R. E. (1997b). Model for the prediction of surface flatness in face milling. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 119(4), 476–484.
Article
Google Scholar
Guzeev, V. I., & Pimenov, D Yu. (2011). Cutting force in face milling with tool wear. Russian Engineering Research, 31(10), 989–993. https://doi.org/10.3103/S1068798X11090139.
Article
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, 10–18. https://doi.org/10.1145/1656274.1656278.
Article
Google Scholar
Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1–11. https://doi.org/10.5121/ijdkp.2015.5201.
Article
Google Scholar
Huang, P. B., Zhang, H.-J., & Lin, Y.-C. (2019a). Development of a Grey online modeling surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 30(4), 1923–1936. https://doi.org/10.1007/s10845-017-1361-z.
Article
Google Scholar
Huang, Y., & Hoshi, T. (2001). Optimization of fixture design with consideration of thermal deformation in face milling. Journal of Manufacturing Systems, 19(5), 332–340. https://doi.org/10.1016/S0278-6125(01)89005-1.
Article
Google Scholar
Huang, Z., Zhu, J., Lei, J., Li, X., & Tian, F. (2019b). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4), 953–966. https://doi.org/10.1007/s10845-019-01488-7.
Article
Google Scholar
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the fourteenth international joint conference on artificial intelligence, Morgan Kaufmann, San Mateo (Vol. 2 (12), pp. 1137–1143).
Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B. G., & M., (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 24(4), 755–762. https://doi.org/10.1007/s10845-012-0623-z.
Article
Google Scholar
Krishnaprasad, K., Sumesh, C. S., & Ramesh, A. (2019). Numerical modeling and multi objective optimization of face milling of AISI 304 steel. Journal of Applied and Computational Mechanics, 5(4), 749–762. https://doi.org/10.22055/JACM.2019.27528.1414.
Article
Google Scholar
Kuncheva, L. I. (2004). Combining pattern classifiers: Methods and algorithms. Hoboken, NJ: Wiley-Interscience.
Book
Google Scholar
Kuram, E., & Ozcelik, B. (2016). Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling. Journal of Intelligent Manufacturing, 27(4), 817–830. https://doi.org/10.1007/s10845-014-0916-5.
Article
Google Scholar
Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5, 42. https://doi.org/10.1186/s40537-018-0151-6.
Article
Google Scholar
Leonard, J. A., & Kramer, M. A. (1991). Radial basis function networks for classifying process faults. IEEE Control Systems, 11(3), 31–38. https://doi.org/10.1109/37.75576.
Article
Google Scholar
Liu, E. A., & Zou, Q. (2011). Machined surface error analysis a face milling approach. Journal of Advanced Manufacturing Systems, 10(2), 293–307. https://doi.org/10.1142/S0219686711002211.
Article
Google Scholar
Machado, Á. R., & Diniz, A. E. (2017). Tool wear analysis in the machining of hardened steels. International Journal of Advanced Manufacturing Technology, 92(9–12), 4095–4109. https://doi.org/10.1007/s00170-017-0455-2.
Article
Google Scholar
Markopoulos, A. P., Manolakos, D. E., & Vaxevanidis, N. M. (2008). Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 19(3), 283–292. https://doi.org/10.1007/s10845-008-0081-9.
Article
Google Scholar
Maudes, J., Bustillo, A., Guerra, A. J., & Ciurana, J. (2017). Random Forest ensemble prediction of stent dimensions in microfabrication processes. International Journal of Advanced Manufacturing Technology, 91(1–4), 879–893. https://doi.org/10.1007/s00170-016-9695-9.
Article
Google Scholar
Mendes-Moreira, J., Soares, C., Jorge, A. M., & De Sousa, J. F. (2012). Ensemble approaches for regression: A survey. ACM Computing Surveys, 45(1), 10. https://doi.org/10.1145/2379776.2379786.
Article
Google Scholar
Mikołajczyk, T., Nowicki, K., Bustillo, A., & Pimenov, D Yu. (2018). Predicting tool life in turning operations using neural networks and image processing. Mechanical Systems and Signal Processing, 104, 503–513. https://doi.org/10.1016/j.ymssp.2017.11.022.
Article
Google Scholar
Mohanraj, T., Shankar, S., Rajasekar, R., Sakthivel, N. R., & Pramanik, A. (2020). Tool condition monitoring techniques in milling process—A review. Journal of Materials Research and Technology, 9(1), 1032–1042. https://doi.org/10.1016/j.jmrt.2019.10.031.
Article
Google Scholar
Mori, M., Fujishima, M., Inamasu, Y., & Oda, Y. (2011). A study on energy efficiency improvement for machine tools. CIRP Annals Manufacturing Technology, 60(1), 145–148. https://doi.org/10.1016/j.cirp.2011.03.099.
Article
Google Scholar
Nadeau, C., & Bengio, Y. (2003). Inference for the generalization error. Machine Learning, 52(3), 239–281. https://doi.org/10.1023/A:1024068626366.
Article
Google Scholar
Nadolny, K., & Kapłonek, W. (2014). Analysis of flatness deviations for austenitic stainless steel workpieces after efficient surface machining. Measurement Science Review, 14(4), 204–212. https://doi.org/10.2478/msr-2014-0028.
Article
Google Scholar
Nguyen, H. T., Wang, H., & Hu, S. J. (2014). Modeling cutter tilt and cutter-spindle stiffness for machine condition monitoring in face milling using high-definition surface metrology. International Journal of Advanced Manufacturing Technology, 70(5–8), 1323–1335. https://doi.org/10.1007/s00170-013-5347-5.
Article
Google Scholar
Oleaga, I., Pardo, C., Zulaika, J. J., & Bustillo, A. (2018). A machine-learning based solution for chatter prediction in heavy-duty milling machines. Measurement: Journal of the International Measurement Confederation, 128, 34–44. https://doi.org/10.1016/j.measurement.2018.06.028.
Article
Google Scholar
Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8.
Article
Google Scholar
Park, K. S., & Kim, S. H. (1998). Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: A review. Artificial Intelligence in Engineering, 12(1–2), 127–134. https://doi.org/10.1016/S0954-1810(97)00011-3.
Article
Google Scholar
Pimenov, D Yu. (2015). Mathematical modeling of power spent in face milling taking into consideration tool wear. Journal of Friction and Wear, 36(1), 45–48. https://doi.org/10.3103/S1068366615010110.
Article
Google Scholar
Pimenov, D Yu, Bustillo, A., & Mikolajczyk, T. (2018a). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing, 29(5), 1045–1061. https://doi.org/10.1007/s10845-017-1381-8.
Article
Google Scholar
Pimenov, D Yu, & Guzeev, V. I. (2017). Mathematical model of plowing forces to account for flank wear using FME modeling for orthogonal cutting scheme. International Journal of Advanced Manufacturing Technology, 89(9–12), 3149–3159. https://doi.org/10.1007/s00170-016-9216-x.
Article
Google Scholar
Pimenov, D Yu, Guzeev, V. I., & Koshin, A. A. (2011a). Elastic displacement of a technological system in face milling with tool wear. Russian Engineering Research, 31(11), 1105–1109. https://doi.org/10.3103/S1068798X11110219.
Article
Google Scholar
Pimenov, D Yu, Guzeev, V. I., & Koshin, A. A. (2011b). Influence of cutting conditions on the stress at tool's rear surface. Russian Engineering Research, 31(11), 1151–1155. https://doi.org/10.3103/S1068798X11110207.
Article
Google Scholar
Pimenov, D Yu, Guzeev, V. I., Krolczyk, G., Mia, M., & Wojciechowski, S. (2018b). Modeling flatness deviation in face milling considering angular movement of the machine tool system components and tool flank wear. Precision Engineering, 54, 327–337. https://doi.org/10.1016/j.precisioneng.2018.07.001.
Article
Google Scholar
Pimenov, D Yu, Guzeev, V. I., Mikolajczyk, T., & Patra, K. (2017). A study of the influence of processing parameters and tool wear on elastic displacements of the technological system under face milling. International Journal of Advanced Manufacturing Technology, 92(9–12), 4473–4486. https://doi.org/10.1007/s00170-017-0516-6.
Article
Google Scholar
Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computing Surveys, 28(1), 71–72. https://doi.org/10.1145/234313.234346.
Article
Google Scholar
Rodrigues, M. A., Hassui, A., Lopes da Silva, R. H., & Loureiro, D. (2016). Tool life and wear mechanisms during Alloy 625 face milling. International Journal of Advanced Manufacturing Technology, 85(5–8), 1439–1448. https://doi.org/10.1007/s00170-015-8056-4.
Article
Google Scholar
Rybicki, M., & Kawalec, M. (2010). Form deviations of hot work tool steel 55NiCrMoV (52HRC) after face finish milling. International Journal of Machining and Machinability of Materials, 7(3–4), 176–192. https://doi.org/10.1504/IJMMM.2010.033065.
Article
Google Scholar
Samanta, B., Erevelles, W., & Omurtag, Y. (2008). Prediction of workpiece surface roughness using soft computing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(10), 1221–1232. https://doi.org/10.1243/09544054JEM1035.
Article
Google Scholar
Sanjay, C., & Jyothi, C. (2006). A study of surface roughness in drilling using mathematical analysis and neural networks. International Journal of Advanced Manufacturing Technology, 29(9–10), 846–852. https://doi.org/10.1007/s00170-005-2538-8.
Article
Google Scholar
Santos, P., Maudes, J., & Bustillo, A. (2018). Identifying maximum imbalance in datasets for fault diagnosis of Gearboxes. Journal of Intelligent Manufacturing, 29(2), 333–351. https://doi.org/10.1007/s10845-015-1110-0.
Article
Google Scholar
Shao, H., Wang, H. L., & Zhao, X. M. (2004). A cutting power model for tool wear monitoring in milling. International Journal of Machine Tools and Manufacture, 44(14), 1503–1509. https://doi.org/10.1016/j.ijmachtools.2004.05.003.
Article
Google Scholar
Shnfir, M., Olufayo, O. A., Jomaa, W., & Songmene, V. (2019). Machinability study of hardened 1045 steel when milling with ceramic cutting inserts. Materials, 12(23), 3974. https://doi.org/10.3390/ma12233974.
Article
Google Scholar
Sick, B. (2002). On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research. Mechanical Systems and Signal Processing, 16(4), 487–546. https://doi.org/10.1006/mssp.2001.1460.
Article
Google Scholar
Simunovic, G., Simunovic, K., & Saric, T. (2013). Modelling and simulation of surface roughness in face milling. International Journal of Simulation Modelling, 12(3), 141–153. https://doi.org/10.2507/IJSIMM12(3)1.219.
Article
Google Scholar
Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89. https://doi.org/10.1016/s0034-4257(97)00083-7.
Article
Google Scholar
Teixidor, D., Grzenda, M., Bustillo, A., & Ciurana, J. (2015). Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. Journal of Intelligent Manufacturing, 26(4), 801–814. https://doi.org/10.1007/s10845-013-0835-x.
Article
Google Scholar
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques (Book). In Data mining: Practical machine learning tools and techniques (pp. 1–621).
Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 139(7), 071018. https://doi.org/10.1115/1.4036350.
Article
Google Scholar
Xu, L., Huang, C., Li, C., Wang, J., Liu, H., & Wang, X. (2020). Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01559-0.
Article
Google Scholar
Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
Yi, W., Jiang, Z., Shao, W., Han, X., & Liu, W. (2015). Error compensation of thin plate-shape part with prebending method in face milling. Chinese Journal of Mechanical Engineering, 28(1), 88–95. https://doi.org/10.3901/CJME.2014.1120.171.
Article
Google Scholar
Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using artificial neural network. Expert Systems with Applications, 37(2), 1755–1768. https://doi.org/10.1016/j.eswa.2009.07.033.
Article
Google Scholar
Zheng, P., Wang, H., Sang, Z., Zhong, R. Y. E., Liu, Y., Yu, S., et al. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150. https://doi.org/10.1007/s11465-018-0499-5.
Article
Google Scholar
Zhu, K. P., Wong, Y. S., & Hong, G. S. (2009). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools and Manufacture, 49(7–8), 537–553. https://doi.org/10.1016/j.ijmachtools.2009.02.003.
Article
Google Scholar