Document Type : Original Research Paper
Authors
Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran.
Abstract
The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.
Keywords
- Maximum energy consumption (MEC)
- Rock engineering system (RES)
- Statistical indicators
- Regression methods
- Building stone
Main Subjects
[1]. Tönshoff H., Hillmann-Apmann, H. & Asche, J. (2002). Diamond tools in stone and civil engineering industry: cutting principles, wear and applications. Diamond and Related Materials, 11(3-6), 736-741.
[2]. Wang C. & Clausen, R. (2002). Marble cutting with single point cutting tool and diamond segments. International Journal of Machine Tools and Manufacture, 42(9), 1045-1054.
[3]. Wang J. (2003). Abrasive waterjet machining of engineering materials.
[4]. Ronggang L., Jianqiao, Y. & Qizhong, L. Research on Sawing Mining Technology of Soft Dimension Stone. In: IOP Conference Series: Earth and Environmental Science, 2021. vol 1. IOP Publishing, p 012013
[5]. Wiemann H., Büttner, A., Ertingshausen, W. & Schwartz, W. (1982). A new method for the rapid and accurate measurement of the tension of frame saw blade. Advances in Ultra Hard Materials Application Technology, 2, 127-138.
[6]. Tumac D. & Shaterpour-Mamaghani, A. (2018). Estimating the sawability of large diameter circular saws based on classification of natural stone types according to the geological origin. International Journal of Rock Mechanics and Mining Sciences, 101, 18-32.
[7]. Sariisik A. & Sariisik, G. (2010). Efficiency analysis of armed-chained cutting machines in block production in travertine quarries. Journal of the Southern African Institute of Mining and Metallurgy, 110(8), 473-480.
[8]. Dagrain F. (2011). Understanding stone cutting mechanisms for the design of new cutting tools sequences and the cutting optimization of chain saw machines in Belgian Blue Stone quarries. Diamante Applicazioni & Tecnologia, 66, 54-67.
[9]. Ucun I., Aslantas, K., Sedat Buyuksagis, I. & Tasgetiren, S. (2012). Determination of specific energy in cutting process using diamond saw blade of natural stone. Energy Education Science and Technology Part A: Energy Science and Research, 28(2), 641-648.
[10]. Falcão Neves P., Costa e Silva, M. & Navarro Torres, V. (2012). Evaluation of elastic deformation energy in stone cutting of Portuguese marbles with a diamond saw. Journal of the Southern African Institute of Mining and Metallurgy, 112(5), 413-418.
[11]. Khoshouei M., Jalalian, M. H. & Bagherpour, R. (2020). The effect of geological properties of dimension stones on the prediction of Specific Energy (SE) during diamond wire cutting operations. Rudarsko-geološko-naftni zbornik, 35(3).
[12]. Pershin G. & Ulyakov, M. (2014). Analysis of the effect of wire saw operation mode on stone cutting cost. Journal of Mining Science, 50, 310-318.
[13]. Di Giovanni A. (2021) Performance analysis of a dimension stone exploitation with chain saw cutting machines: the “Penna dei Corvi” quarry case study. Politecnico di Torino,
[14]. Dong P., Zhang, J., Ouyang, C., Sun, D. & Wu, J. (2021). Investigation on sawing performance of diamond frame saw based on reciprocating swing in processing hard stone. Journal of Materials Processing Technology, 295, 117171.
[15]. Tumac D., Avunduk, E., Copur, H., Bilgin, N. & Balci, C. Estimation of the performance of chain saw machines from shore hardness and the other mechanical properties. In: ISRM SINOROCK 2013, 2013. OnePetro,
[16]. Primavori P. (2006). Uses for the chain saw. Marmo Mach Int, 53, 80-102.
[17]. Howarth D. F. & Rowlands, J. C. (1986). Development of an index to quantify rock texture for qualitative assessment of intact rock properties. Geotechnical Testing Journal, 9(4), 169-179.
[18]. Segade Robleda A., Vilán Vilán, J. A., López Lago, M. & Taboada Castro, J. (2010). The rock processing sector: part i: cutting technology tools, a new diamond segment band saw part ii: study of cutting forces. Dyna, 77(161), 77-87.
[19]. Taylor R. W. (1976) An investigation into the wear characteristics of bandsaw blades and their influence on the sawing rates and costs of bandsaw operations. Sheffield Hallam University (United Kingdom),
[20]. Ceylanoğlu A. & Görgülü, K. (2020) The performance measurement results of stone cutting machines and their relations with some material properties. In: Mine Planning and Equipment Selection 1997. CRC Press, pp 393-398
[21]. Mikaeil R., Sohrabian, B. & Ataei, M. (2018). The study of energy consumption in the dimension stone cutting process. Rudarsko-geološko-naftni zbornik, 33(4).
[22]. Sariisik A. & Sariisik, G. (2013). Investigation of the cutting performance of the natural stone block production in quarries with armed-chain cutting machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(6), 1291-1301.
[23]. Lindawati L. & Fitriadi, N. (2018). Analysis of noise level generated by stone cutter machine a case study in marble production unit, South Aceh. Jurnal Inotera, 3(1), 53-58.
[24]. Bilim N. (2012). Optimum cutting speed of block-cutting machines in natural stones for energy saving. Journal of Central South University, 19, 1234-1239.
[25]. Yurdakul M. & Akdas, H. (2012). Prediction of specific cutting energy for large diameter circular saws during natural stone cutting. International Journal of Rock Mechanics and Mining Sciences, 53, 38-44.
[26]. Soltani H. M. & Tayebi, M. (2020). Determination of wear parameters and mechanisms of diamond/copper tools in marble stones cutting. International Journal of Refractory Metals and Hard Materials, 87, 105172.
[27]. Ding Z. & Yu, C. Improved design of stone cutting machine based on disassembly analysis. In: 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2016), 2017. Atlantis Press, pp 25-31
[28]. Lons H. (1970). Basic research on frame sawing with diamond blades. Diss3 Tu Hanover.
[29]. Mancini R., Cardu, M., Fornaro, M., Linares, M. & Peila, D. (1992) Analysis and simulation of stone cutting with microtools. In: Titolo volume non avvalorato. Società Italiana Gallerie-Associazione Mineraria Subalpina, pp 227-236
[30]. Mancini R., Linares, M., Cardu, M., Fornaro, M. & Bobbio, M. Simulation of the operation of a rock chain cutter on statistical models of inhomogenous rocks. In: Proc. of the 3rd Int. Symp. on Mine Planning and Equipment Selection, Istanbul, Turkey, October, 1994. p 468
[31]. Copur H., Balci, C., Tumac, D. & Bilgin, N. (2011). Field and laboratory studies on natural stones leading to empirical performance prediction of chain saw machines. International Journal of Rock Mechanics and Mining Sciences, 48(2), 269-282.
[32]. Copur H. (2010). Linear stone cutting tests with chisel tools for identification of cutting principles and predicting performance of chain saw machines. International Journal of Rock Mechanics and Mining Sciences, 47(1), 104-120.
[33]. Neves P. F., e Silva, M. C., Paneiro, G. & Frazão, M. (2016). Prediction of slab production with multiblade Gang Saw. International Multidisciplinary Scientific GeoConference: SGEM, 2, 681-686.
[34]. Bayram F. (2013). Prediction of sawing performance based on index properties of rocks. Arabian Journal of Geosciences, 6, 4357-4362.
[35]. Mikaeil R., Ataei, M., Ghadernejad, S. & Sadegheslam, G. (2014). Predicting the relationship between system vibration with rock brittleness indexes in rock sawing process. Archives of Mining Sciences, 59(1), 139-153.
[36]. Korman T., Kujundžić, T. & Kuhinek, D. (2015). Simulation of the chain saw cutting process with a linear cutting machine. International journal of rock mechanics and mining sciences, 78, 283-289.
[37]. Dormishi A., Ataei, M., Mikaeil, R. & Kakaei, R. K. (2018). Relations between texture coefficient and energy consumption of gang saws in carbonate rock cutting process. Civil Engineering Journal, 4(2), 413-421.
[38]. Ziaei J., Ghadernejad, S., Jafarpour, A. & Mikaeil, R. (2020). A Modified Schimazek’s F-abrasiveness Factor for Evaluating Abrasiveness of Andesite Rocks in Rock Sawing Process. Journal of Mining and Environment, 11(2), 563-575.
[39]. Mikaeil R., Esmaeilzade, A. & Shaffiee Haghshenas, S. (2021). Investigation of the relationship between schimazek's f-abrasiveness factor and current consumption in rock cutting process. JCEMA, 5(2), 47-55.
[40]. Shaffiee Haghshenas S., Mikaeil, R., Esmaeilzadeh, A., Careddu, N. & Ataei, M. (2022). Statistical Study to Evaluate Performance of Cutting Machine in Dimension Stone Cutting Process. Journal of Mining and Environment, 13(1), 53-67.
[41]. Mohammadi J., Ataei, M., Kakaei, R. K., Mikaeil, R. & Haghshenas, S. S. (2018). Prediction of the production rate of chain saw machine using the multilayer perceptron (MLP) neural network. Civil Engineering Journal, 4(7), 1575-1583.
[42]. Mikaeil R., Haghshenas, S. S., Haghshenas, S. S. & Ataei, M. (2018). Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Computing and Applications, 29, 283-292.
[43]. Mikaeil R., Ataei, M. & Yousefi, R. (2011). Application of a fuzzy analytical hierarchy process to the prediction of vibration during rock sawing. Mining Science and Technology (China), 21(5), 611-619.
[44]. Sun D., Zhang, J., Wu, J. & Dong, P. (2021). A hybrid model for evaluating the sawability of stones through the performance of frame sawing machine. Measurement, 181, 109588.
- Aryafar A. & Mikaeil, R. (2016). Estimation of the ampere consumption of dimension stone sawing machine using of artificial neural networks. International Journal of Mining and Geo-Engineering, 50(1), 121-130.
[46]. Tumac D. (2016). Artificial neural network application to predict the sawability performance of large diameter circular saws. Measurement, 80, 12-20.
[47]. Asiltürk İ. & Ünüvar, A. (2009). Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system. journal of materials processing technology, 209(5), 2302-2313.
[48]. Zhuo R., Deng, Z., Chen, B., Liu, T., Ge, J., Liu, G. & Bi, S. (2022). Research on online intelligent monitoring system of band saw blade wear status based on multi-feature fusion of acoustic emission signals. The International Journal of Advanced Manufacturing Technology, 121(7-8), 4533-4548.
[49]. Almasi S. N., Bagherpour, R., Mikaeil, R., Ozcelik, Y. & Kalhori, H. (2017). Predicting the building stone cutting rate based on rock properties and device pullback amperage in quarries using M5P model tree. Geotechnical and Geological Engineering, 35, 1311-1326.
[50]. Çinar S. M. (2022). Developing hierarchical fuzzy logic controllers to improve the energy efficiency and cutting rate stabilization of natural stone block-cutting machines. Journal of Cleaner Production, 355, 131799.
[51]. Yurdakul M., Gopalakrishnan, K. & Akdas, H. (2014). Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. International Journal of Rock Mechanics and Mining Sciences, 67, 127-135.
[52]. Çınar S. M., Çimen, H. & Büyüksağiş, İ. S. Improvement of energy efficiency using a multi-input fuzzy logic controller in a stone cutting machine. In, 2018. ASTM,
[53]. Faramarzi F., Ebrahimi Farsangi, M. & Mansouri, H. (2013). An RES-based model for risk assessment and prediction of backbreak in bench blasting. Rock mechanics and rock engineering, 46, 877-887.
[54]. Fattahi H. & Moradi, A. (2017). Risk assessment and estimation of TBM penetration rate using RES-based model. Geotechnical and Geological Engineering, 35, 365-376.
[55]. Azadmehr A., Jalali, S. M. E. & Pourrahimian, Y. (2019). An application of rock engineering system for assessment of the rock mass fragmentation: a hybrid approach and case study. Rock Mechanics and Rock Engineering, 52(11), 4403-4419.
[56]. Saeidi O., Azadmehr, A. & Torabi, S. R. (2014). Development of a rock groutability index based on the Rock Engineering Systems (res): a case study. Indian Geotechnical Journal, 44, 49-58.
[57]. Zhou Q., Herrera, J. & Hidalgo, A. (2019). Development of a quantitative assessment approach for the coal and gas outbursts in coal mines using rock engineering systems. International Journal of Mining, Reclamation and Environment, 33(1), 21-41.
[58]. Fattahi H. & Babanouri, N. (2018). RES-based model in evaluation of surface settlement caused by EPB shield tunneling. Indian Geotechnical Journal, 48, 746-752.
[59]. Fattahi H. (2018). Applying rock engineering systems to evaluate shaft resistance of a pile embedded in rock. Geotechnical and Geological Engineering, 36, 3269-3279.
[60]. Meten M., Bhandary, N. P. & Yatabe, R. (2015). Application of GIS-based fuzzy logic and rock engineering system (RES) approaches for landslide susceptibility mapping in Selelkula area of the Lower Jema River Gorge, Central Ethiopia. Environmental earth sciences, 74, 3395-3416.
[61]. Saffari A., Sereshki, F., Ataei, M. & Ghanbari, K. (2013). Applying rock engineering systems (RES) approach to evaluate and classify the coal spontaneous combustion potential in Eastern Alborz coal mines. International Journal of Mining and Geo-Engineering, 47(2), 115-127.
[62]. Hasanipanah M., Jahed Armaghani, D., Monjezi, M. & Shams, S. (2016). Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environmental Earth Sciences, 75, 1-12.
[63]. Fattahi H. & Moradi, A. (2018). A new approach for estimation of the rock mass deformation modulus: a rock engineering systems-based model. Bulletin of engineering geology and the environment, 77, 363-374.
[64]. Ghanbari K., Ataei, M., Sereshki, F. & Saffari, A. (2018). Determination and assessment of coal bed methane potential using rock engineering systems. Journal of Mining and Environment, 9(3), 605-621.
[65]. Fattahi H. (2017). Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems. Environmental earth sciences, 76(5), 224.
[66]. Dormishi A., Ataei, M., Khaloo Kakaie, R., Mikaeil, R. & Shaffiee Haghshenas, S. (2019). Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. Journal of Mining and Environment, 10(2), 543-557.
[67]. Kremelberg D. (2010) Practical statistics: A quick and easy guide to IBM® SPSS® Statistics, STATA, and other statistical software. SAGE publications,
[68]. Seber G. A. & Lee, A. J. (2003) Linear regression analysis, vol 330. John Wiley & Sons,
[69]. Hudson J. (1992) Rock engineering systems. Theory and practice.
[70]. KhaloKakaie R. & Zare Naghadehi, M. (2012). Ranking the rock slope instability potential using the Interaction Matrix (IM) technique; a case study in Iran. Arabian Journal of Geosciences, 5(2), 263-273.
[71]. Lu P. & Latham, J.-P. A continuous quantitative coding approach to the interaction matrix in rock engineering systems based on grey systems approaches. In: International congress International Association of Engineering Geology, 1994. pp 4761-4770
[72]. Benardos A. & Kaliampakos, D. (2004). A methodology for assessing geotechnical hazards for TBM tunnelling—illustrated by the Athens Metro, Greece. International Journal of Rock Mechanics and Mining Sciences, 41(6), 987-999.
[73]. Fattahi H. (2016). Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Engineering with Computers, 32(4), 567-580.
[74]. Fattahi H. (2016). Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. Geosciences Journal, 20(5), 681–690.
[75]. Fattahi H. (2017). Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values. Computational Geosciences, 21(4), 665-681.
[76]. Babanouri N. & Fattahi, H. (2018). Evaluating orthotropic continuum analysis of stress wave propagation through a jointed rock mass. Bulletin of engineering geology and the environment, 72(2), 725-733.
)