[
[1] Wang, M., Xi, L., Du, S. (2014). 3D surface form error evaluation using high definition metrology. Precision Engineering, 38, 230-236.10.1016/j.precisioneng.2013.08.008
]Search in Google Scholar
[
[2] Pimenov, D.Y., Guzeev, V.I., Krolczyk, G., Mozammel, M., Wojciechowski, S. (2018). Modeling flatness deviation in face milling considering angular movement of the machine tool system components and tool flank wear. Precision Engineering, 54, 327-337.10.1016/j.precisioneng.2018.07.001
]Search in Google Scholar
[
[3] Chuchala, D., Dobrzynski, M., Pimenov, D.Y., Orlowski, K.A., Krolczyk, G., Giasin, K. (2021). Surface roughness evaluation in thin EN AW-6086-T6 alloy plates after face milling process with different strategies. Materials, 14 (11), 3036.10.3390/ma14113036819975634199651
]Search in Google Scholar
[
[4] Ramasamy, S.K. (2011). Multi-scale data fusion for surface metrology. PhD Dissertation, The University of North Carolina, Charlotte, USA.
]Search in Google Scholar
[
[5] Van Gestel, N. (2011). Determining measurement uncertainties of feature measurements on CMMs. PhD Dissertation, Katholieke University Leuven, Belgium.
]Search in Google Scholar
[
[6] Brown, C.A., Hansen, H.N., Jiang, X.J., Blateyron, F., Berglund, J., Senin, N., Stemp, W.J. (2018). Multiscale analyses and characterizations of surface topographies. CIRP Annals, 67 (2), 839-862.10.1016/j.cirp.2018.06.001
]Search in Google Scholar
[
[7] Štrbac, B., Ačko, B., Havrlišan, S., Matin, I., Savković, B., Hadžistević, M. (2020). Investigation of the effect of temperature and other significant factors on systematic error and measurement uncertainty in CMM measurements by applying design of experiments. Measurement, 158, 107692.10.1016/j.measurement.2020.107692
]Search in Google Scholar
[
[8] Durakbasa, M.N., Osanna., P.H., Demircioglu, P. (2011). The factors affecting surface roughness measurements of the machined flat and spherical surface structures – The geometry and the precision of the surface. Measurement, 44 1986-1999.10.1016/j.measurement.2011.08.020
]Search in Google Scholar
[
[9] Zhang, M., Liu, Y., Sun, C., Wang, X., Tan, J. (2020). Precision measurement and evaluation of flatness error for the aero-engine rotor connection surface based on convex hull theory and an improved PSO algorithm. Measurement Sciences and Technology, 31 (8), 085006.10.1088/1361-6501/ab8170
]Search in Google Scholar
[
[10] Du, Z., Zhu, M., Wu, Z., Yang, J. (2016). Measurement uncertainty on the circular features in coordinate measurement system based on the error ellipse and Monte Carlo methods. Measurement Science and Technology, 27 (12),125016.10.1088/0957-0233/27/12/125016
]Search in Google Scholar
[
[11] Hadžistević, M., Štrbac, B., Spasić Jokić, V., Delić, M., Sekulić, M., Hodolič, J. (2015). Factors of estimating flatness error as a surface requirement of exploitation. Metallurgy, 54 (1), 239-242.
]Search in Google Scholar
[
[12] Bešić, I., Van Gestel, N., Kruth, J.P., Bleys, P., Hodolič, J. (2011). Accuracy improvement of laser line scanning for feature measurements on CMM. Optics and Lasers in Engineering, 49 (11), 1274-1280.10.1016/j.optlaseng.2011.06.009
]Search in Google Scholar
[
[13] Dhanish, P.B., Mathew, J. (2006). Effect of CMM point coordinate uncertainty on uncertainties in determination of circular features. Measurement, 39, 522-531.10.1016/j.measurement.2005.12.005
]Search in Google Scholar
[
[14] International Organization for Standardization. (2011). Geometrical product specifications (GPS) - Flatness - Part 2: Specification operators. ISO 12781-2:2011.
]Search in Google Scholar
[
[15] Colosimo, B.M., Moroni, G., Petro, S. (2010). A tolerance interval based criterion for optimizing discrete point sampling strategies. Precision Engineering, 34, 745-754.10.1016/j.precisioneng.2010.04.004
]Search in Google Scholar
[
[16] Badar, A.M., Raman, S., Pulat, P.S. (2005). Experimental verification of manufacturing error pattern and its utilization in form tolerance sampling. International Journal of Machine Tools & Manufacture, 45, 63-73.10.1016/j.ijmachtools.2004.06.017
]Search in Google Scholar
[
[17] Raghunandan, R., Rao, P.V. (2007). Selection of an optimum sample size for flatness error estimation while using coordinate measuring machine. International Journal of Machine Tools & Manufacture, 47, 477-482.10.1016/j.ijmachtools.2006.06.008
]Search in Google Scholar
[
[18] Raghunandan, R., Rao, P.V. (2008). Selection of sampling points for accurate evaluation of flatness error using coordinate measuring machine. Journal of Materials Processing Technology, 202 (1-3), 240-245.10.1016/j.jmatprotec.2007.09.066
]Search in Google Scholar
[
[19] Nadolny, K., Kapłonek, W. (2014). Analysis of flatness deviations for austenitic stainless steel workpieces after efficient surface machining. Measurement Sciences Review, 14 (4), 204-212.10.2478/msr-2014-0028
]Search in Google Scholar
[
[20] International Organization for Standardization. (2002). Geometrical Product Specifications (GPS) — Indication of surface texture in technical product documentation. ISO 1302:2002.
]Search in Google Scholar
[
[21] Bartkowiak, T., Staniek, R. (2017). Application of order statistics in the evaluation of flatness error: Sampling problem. In ASME International Mechanical Engineering Congress and Exposition, V002T02A097. ISBN 978-0-7918-5835-6.10.1115/IMECE2017-71295
]Search in Google Scholar
[
[22] Radlovački, V., Hadžistević, M., Štrbac, B., Delić, M., Kamberović, B. (2016). Evaluating minimum zone flatness error using new method - Bundle of plains through one-point. Precision Engineering, 43, 554-562.10.1016/j.precisioneng.2015.10.002
]Search in Google Scholar
[
[23] Štrbac, B., Mikó, B., Rodić, D., Nagy, J., Hadžistević, M. (2020). Analysis of characteristics of noncommercial software systems for assessing flatness error by means of minimum zone method. Technical Gazette, 27 (2), 535-541.
]Search in Google Scholar
[
[24] Montgomery, D.C. (2009). Design and Analysis of Experiment (7th ed.), John Wiley and Sons. ISBN 978-0470398821.
]Search in Google Scholar
[
[25] Mikó, B. (2021). Assessment of flatness error by regression analysis. Measurement, 171, 108720.10.1016/j.measurement.2020.108720
]Search in Google Scholar
[
[26] Zadeh, A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1 (1), 3-28.10.1016/0165-0114(78)90029-5
]Search in Google Scholar
[
[27] Sheth, S., George, P.M. (2016). Experimental investigation and fuzzy modelling of flatness and surface roughness for WCB material using face milling operation. In CAD/CAM, Robotics and Factories of the Future. Springer, 769-777. ISBN 978-81-322-2738-0.10.1007/978-81-322-2740-3_74
]Search in Google Scholar
[
[28] Jang, J.S.R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665-685.10.1109/21.256541
]Search in Google Scholar
[
[29] Takagi, T., Michio, S. (1993). Fuzzy identification of systems and its applications to modeling and control. In Readings in Fuzzy Sets for Intelligent Systems. Elsevier, 387-403. ISBN 978-1-4832-1450-4.10.1016/B978-1-4832-1450-4.50045-6
]Search in Google Scholar
[
[30] Bustillo, A., Pimenov, D.Y., Mia, M., Kapłonek, W. (2021). Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. Journal of Intelligent Manufacturing, 32, 895-912.10.1007/s10845-020-01645-3
]Search in Google Scholar
[
[31] Sheth, S., George, P.M. (2016). Experimental investigation and prediction of flatness and surface roughness during face milling operation of WCB material. Procedia Technology, 23, 344-351.10.1016/j.protcy.2016.03.036
]Search in Google Scholar
[
[32] 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), 237-257.10.1007/s00170-009-2191-8
]Search in Google Scholar
[
[33] Jang, J.S. (1992). Neuro-fuzzy Modeling: Architectures, Analyses, and Applications. Ph.D. Dissertation, University of California Berkeley, USA.
]Search in Google Scholar
[
[34] Zaman, M., Hassan, A. (2019). Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering. Neural Computing and Applications, 31, 5935-5949.10.1007/s00521-018-3388-2
]Search in Google Scholar
[
[35] Yin, S., Nguyen, D., Chen, F., Tang, Q., Duc, L.A. (2019). Application of compressed air in the online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy. Journal of Advanced Manufacturing Technology, 101, 1315-1331.10.1007/s00170-018-2909-6
]Search in Google Scholar
