Almasi SN, Bagherpour R, Mikaeil R, Ozcelik Y (2017) Analysis of bead wear in diamond wire sawing considering the rock properties and production rate. Bull Eng Geol Environ 76(4):1593-1607. https://doi.org/10.1007/s10064-017-1057-9
Ataei M, Mikaiel R, Sereshki F, Ghaysari N (2012) Predicting the production rate of diamond wire saw using statistical analysis. Arab J Geosci 5(6):1289-1295. https://doi.org/10.1007/s12517-010-0278-z
Atai M (2009) Building stones. Shahroud University of Technology Publications, Shahroud, Iran.
Bagherpour R, Khademian A, Almasi SN, Aalaei M (2014) Optimum cutting wire assembly in dimension stone quarries. J Mining Metall Section A:Mining 50(1):1-8.
Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Know Discov 2(2):121–167. https://doi.org/10.1023/A:1009715923555
Careddu N, Perra ES, Masala O (2019) Diamond wire sawing in ornamental basalt quarries: technical, economic and environmental considerations. Bull Eng Geol Environ 78(1):557-568. https://doi.org/10.1007/s10064-017-1112-6
Holland JH (1992) Adaptation in Natural and Artificial Systems. MIT Press.
Huang G, Xu X (2013) Sawing performance comparison of brazed and sintered diamond wires. Chin J Mech Eng 26(2):393-399. https://doi.org/10.3901/CJME.2013.02.393
Jain S, Rathore SS (2011) Prediction of cutting performance of diamond wire saw machine in quarrying of marble: a neural network approach. Rock Mech Rock Engin 44(3):367-371. https://doi.org/10.1007/s00603-011-0137-6
Jalil K, Raza S (2019) Cost Estimation for Bench Drilling Phase of Diamond Wire Sawing Technique for Granite Mining. Int J Sci Res Public 9(3):2250-3153.
Kumar A, Suresh Y (2016) Multilayer feed forward neural network to predict the speed of wind. 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE. 10.1109/CSITSS.2016.7779372
Luo J, Ying K, Bai J (2005) Savitzky–Golay smoothing and differentiation filter for even number data. Sig process 85(7):1429-1434. https://doi.org/10.1016/j.sigpro.2005.02.002
Mikaeil R, Haghshenas SS, Ozcelik Y, Gharehgheshlagh H (2018) Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotech Geol Eng 36(6):3779-379. https://doi.org/10.1007/s10706-018-0571-2
Özçelik Y (2005) Optimum working conditions of diamond wire cutting machines in the marble industry IDR. Indus Diam Rev 1:58-64.
Savitzky A, Golay MJ (1964) Smoothing and Differentiation of Data by Simplified Leas Squares Procedures. Analy Chem 36:1627–1639. https://doi.org/10.1021/ac60214a047
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Yilmazkaya E, Dagdelenler G, Ozcelik Y, Sonmez H (2018) Prediction of mono-wire cutting machine performance parameters using artificial neural network and regression models. Engin Geolog 239:96-108. https://doi.org/10.1016/j.enggeo.2018.03.009
Yilmazkaya E, Ozcelik Y (2016) The Effects of Operational Parameters on a Mono-wire Cutting System: Efficiency in Marble Processing. Rock Mech Rock Engin 49:523-539. https://doi.org/10.1007/s00603-015-0743-9
Zhang H, Zhang J, Chen M, An Q (2019) The effect of operational parameters on diamond tools of frame sawing system: Wear characteristics and optimization in stone processing. Int J Refrac Met Hard Mater 84:105019. https://doi.org/10.1016/j.ijrmhm.2019.105019
Zhong Z, Carr TR (2016) Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2–Reservoir oil minimum miscibility pressure prediction. Fuel 184:590-603. https://doi.org/10.1016/j.fuel.2016.07.030