Development of Intelligent Systems to Predict Diamond Wire Saw Performance

Document Type : Regular Article


1 Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran

2 Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran

3 Department of Mining Engineering, Hacettepe University, Ankara, Turkey

4 Graduate of Civil Engineering, Department of Civil Engineering, Islamic Azad University, Astara Branch, Astara, Iran


Assessment of wear rate is an inseparable section of the saw ability of dimension stone, and an essential task to optimization in the diamond wire saw performance. This research aims to provide an accurate, practical and applicable model for predicting the wear rate of diamond bead based on rock properties using applications and performances of intelligent systems. In order to reach this purpose, 38 cutting test results with 38 different rocks were used from andesites, limestones and real marbles quarries located in eleven areas in Turkey. Prediction of wear rate is determined by optimization techniques like Multilayer Perceptron (MLP) and hybrid Genetic algorithm –Artificial neural network (GA-ANN) models that were utilized to build two estimation models by MATLAB software. In this study, 80% of the total samples were used randomly for the training dataset, and the remaining 20% was considered as testing data for GA-ANN model. Further, accuracy and performance capacity of models established were investigated using root mean square error (RMSE), the coefficient of determination (R2) and standard deviation (STD). Finally, a comparison was made among performances of these soft computing techniques for predicting and the results obtained indicated hybrid GA-ANN model with a coefficient of determination (R2) of training = 0.95 and testing = 0.991 can get more accurate predicting results in comparison with MLP models.


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[1]     Ozcelik Y, Polat E, Bayram F, Ay AM. Investigation of the effects of textural properties on marble cutting with diamond wire. Int J Rock Mech Min Sci 2004;41:228–34.
[2]     Luo SY, Liao YS. Effects of diamond grain characteristics on sawblade wear. Int J Mach Tools Manuf 1993;33:257–66. doi:10.1016/0890-6955(93)90078-9.
[3]     Özçelik Y. Multivariate Statistical Analysis of the Wear on Diamond Beads in the Cutting of Andesitic Rocks. Key Eng Mater 2003;250:118–30. doi:10.4028/
[4]     Mikaeil R, Ozcelik Y, Ataei M, Shaffiee Haghshenas S. Application of harmony search algorithm to evaluate performance of diamond wire saw. J Min Environ 2016. doi:10.22044/jme.2016.723.
[5]     Luo SY, Liao YS. Study of the behaviour of diamond saw-blades in stone processing. J Mater Process Technol 1995;51:296–308. doi:10.1016/0924-0136(94)01603-X.
[6]     Luo SY. Characteristics of diamond sawblade wear in sawing. Int J Mach Tools Manuf 1996;36:661–72. doi:10.1016/0890-6955(95)00071-2.
[7]     Luo SY. Investigation of the worn surfaces of diamond sawblades in sawing granite. J Mater Process Technol 1997;70:1–8. doi:10.1016/S0924-0136(97)00033-2.
[8]     Xu X. Study on the thermal wear of diamond segmented tools in circular sawing of granites. Tribol Lett 2001;10:245–50. doi:10.1023/A:1016614231427.
[9]     Wei X, Wang CY, Zhou ZH. Study on the fuzzy ranking of granite sawability. J Mater Process Technol 2003;139:277–80. doi:10.1016/S0924-0136(03)00235-8.
[10]    Ersoy A, Buyuksagic S, Atici U. Wear characteristics of circular diamond saws in the cutting of different hard abrasive rocks. Wear 2005;258:1422–36. doi:10.1016/j.wear.2004.09.060.
[11]    Özçelik Y, Kulaksız S. Investigation of the relationship between cutting angles and wear on beads in diamond wire cutting method. 9th Mine Plan. Equip. Sel. Symp. Athens, Greece, 2000, p. 6–9.
[12]    Özçelik Y, Kulaksız S, Çetin M. Assessment of the wear of diamond beads in the cutting of different rock types by the ridge regression. J Mater Process Technol 2002;127:392–400. doi:10.1016/S0924-0136(02)00429-6.
[13]    Najmedin Almasi S, Bagherpour R, Mikaeil R, Ozcelik Y. Analysis of bead wear in diamond wire sawing considering the rock properties and production rate. Bull Eng Geol Environ 2017;76:1593–607. doi:10.1007/s10064-017-1057-9.
[14]    Shaffiee Haghshenas S, Ozcelik Y, Shaffiee Haghshenas S, Mikaeil R, Sirati P. Ranking and Assessment of Tunneling Projects Risks Using Fuzzy MCDM (Case Study: Toyserkan Doolayi Tunnel). 25th Int. Min. Congr. Exhib. Turkey, 2017, p. 122–8.
[15]    Haghshenas SS, Neshaei MAL, Pourkazem P, Haghshenas SS. The Risk Assessment of Dam Construction Projects Using Fuzzy TOPSIS (Case Study: Alavian Earth Dam). Civ Eng J 2016;2:158–67.
[16]    Haghshenas SS, Haghshenas SS, Barmal M, Farzan N. Utilization of soft computing for risk assessment of a tunneling project using geological units. Civ Eng J 2016;2:358–64.
[17]    Rad MY, Haghshenas SS, Haghshenas SS. Mechanostratigraphy of cretaceous rocks by fuzzy logic in East Arak, Iran. 4th Int. Work. Comput. Sci. Eng. WCSE, 2014.
[18]    Onyari EK, Ilunga FM. Application of MLP neural network and M5P model tree in predicting streamflow: A case study of Luvuvhu catchment, South Africa. Int J Innov Manag Technol 2013;4:11.
[19]    Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989;2:359–66. doi:
[20]    Daniels H, Kamp B. Application of MLP Networks to Bond Rating and House Pricing. Neural Comput Appl 1999;8:226–34. doi:10.1007/s005210050025.
[21]    Khotanzad A, Chung C. Application of multi-layer perceptron neural networks to vision problems. Neural Comput Appl 1998;7:249–59. doi:10.1007/BF01414886.
[22]    Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A. Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 2006;43:224–35. doi:10.1016/j.ijrmms.2005.06.007.
[23]    Hecht-Nielsen R. Kolmogorov’s mapping neural network existence theorem. Proc. Int. Conf. Neural Networks, IEEE Press; 1987, p. 11–4.
[24]    Hush. Classification with neural networks: a performance analysis. IEEE Int. Conf. Syst. Eng., IEEE; 1989, p. 277–80. doi:10.1109/ICSYSE.1989.48672.
[25]    Kaastra I, Boyd M. Designing a neural network for forecasting financial and economic time series. Neurocomputing 1996;10:215–36. doi:10.1016/0925-2312(95)00039-9.
[26]    Kanellopoulos I, Wilkinson GG. Strategies and best practice for neural network image classification. Int J Remote Sens 1997;18:711–25.
[27]    Ripley BD. Statistical aspects of neural networks. Networks Chaos—statistical Probabilistic Asp 1993;50:40–123.
[28]    Paola JD. Neural network classification of multispectral imagery. Master Thesisi, The University of Arizona, USA, 1994.
[29]    Wang C. A theory of generalization in learning machines with neural network applications. Ph.D. Thesis, University of Pennsylvania, 1994.
[30]    Masters T. Practical neural network recipes in C++. Academic Press, San Diego, CA.; 1993.
[31]    Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 1994;5:989–93. doi:10.1109/72.329697.
[32]    Jahed Armaghani D, Hasanipanah M, Tonnizam Mohamad E. A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 2016;32:155–71. doi:10.1007/s00366-015-0408-z.
[33]    Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S. Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 2008;96:141–58. doi:10.1016/j.enggeo.2007.10.009.
[34]    Mikaeil R, Haghshenas SS, Shirvand Y, Hasanluy MV, Roshanaei V. Risk assessment of geological hazards in a tunneling project using harmony search algorithm (case study: Ardabil-Mianeh railway tunnel). Civ Eng J 2016;2:546–54.
[35]    Mikaeil R, Haghshenas SS, Haghshenas SS, Ataei M. Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Comput Appl 2018;29:283–92. doi:10.1007/s00521-016-2557-4.
[36]    Haghshenas SS, Haghshenas SS, Mikaeil R, Sirati Moghadam P, Haghshenas AS. A new model for evaluating the geological risk based on geomechanical properties—case study: the second part of emamzade hashem tunnel. Electron J Geotech Eng 2017;22:309–20.
[37]    Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press; 1992.
[38]    Terranova OG, Gariano SL, Iaquinta P, Iovine GGR. GA SAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model. Geosci Model Dev 2015;8:1955–78.
[39]    Woodward M, Kapelan Z, Gouldby B. Adaptive flood risk management under climate change uncertainty using real options and optimization. Risk Anal 2014;34:75–92.
[40]    Saemi M, Ahmadi M, Varjani AY. Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 2007;59:97–105. doi:10.1016/j.petrol.2007.03.007.
[41]    Khandelwal M, Armaghani DJ. Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique. Geotech Geol Eng 2016;34:605–20. doi:10.1007/s10706-015-9970-9.
[42]    Momeni E, Nazir R, Jahed Armaghani D, Maizir H. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 2014;57:122–31. doi:10.1016/j.measurement.2014.08.007.
[43]    Monjezi M, Amini Khoshalan H, Yazdian Varjani A. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 2012;5:441–8. doi:10.1007/s12517-010-0185-3.
[44]    Aghajanloo M-B, Sabziparvar A-A, Hosseinzadeh Talaee P. Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl 2013;23:1387–93. doi:10.1007/s00521-012-1087-y.