Statistical Analysis and Optimization of Drilling Process using Response Surface Methodology and Experimental Data | ||
| Journal of Mining and Environment | ||
| مقاله 4، دوره 15، شماره 1، فروردین 2024، صفحه 55-71 اصل مقاله (4.43 M) | ||
| نوع مقاله: Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.13344.2454 | ||
| نویسندگان | ||
| Rym Khettabi1؛ Issam Touil1؛ Mohamed Kezzar* 1؛ Mohamed R. Eid2، 3؛ Fatima.Z Derdour1؛ Kamel Khounfais4؛ Lakhdar Khochmane1 | ||
| 1Mechanical Engineering Department, University of 20 Août 1955 Skikda, El Hadaiek Road, Skikda, Algeria | ||
| 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Al-Wadi Al-Gadid, Egypt | ||
| 3Finance and Insurance Department, College of Business Administration, Northern Border University, Arar, Saudi Arabia | ||
| 4Physical Engineering Department, University of 20 Août 1955 Skikda, El Hadaiek Road, Skikda, Algeria | ||
| چکیده | ||
| It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity of the bit, RPM, cutting angle , and rock resistance on the penetration rate of the drilling tool. In this examination, three kinds of limestone rocks were considered. The planned assessments were carried out at three stages of the considered four input variables. The statistical analysis was realized using both RSM approach and analysis of variance (ANOVA). This analysis allowed us to develop the appropriate penetration model with a higher determination coefficient of 96.19%, which demonstrates the high correlation between the predicted and experimental data, and consequently, it can be concluded that the obtained model is highly suitable for the prediction of the penetration rate. Also from variance analysis, the results obtained show that rotational speed, RPM, and weight on the bit (WOB) parameters, as well as the nature of the rock, which is determined by the rock compressive resistance, having a significant effect on the penetration rate; however, the rake angle has little effect. Finally, the optimal parameters were determined to find the best possible penetration rate of the drilling tool. | ||
| کلیدواژهها | ||
| Optimization؛ Experimental data؛ Drilling parameters؛ Optimal parameters؛ RMS and ANOVA | ||
| مراجع | ||
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[1]. Tuna, E. (2010). Real-time-optimization of drilling parameters during drilling operations (Thesis). Middle East Technical University.
[2]. Saeidi, O., Torabi, S. R., Ataei, M., and Rostami, J. (2014). A stochastic penetration rate model for rotary drilling in surface mines. International Journal of Rock Mechanics and Mining Sciences 68: 55-65.
[3]. Saeidi, O., Rostami, J., Ataei, M., and Torabi, S. R. (2014). Use of digital image processing techniques for evaluating wear of cemented carbide bits in rotary drilling. Automation in Construction 44: 140-151.
[4]. Shad, H. I. A., Sereshki, F., Ataei, M., and Karamoozian, M. (2018). Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system. International Journal of Mining Science and Technology 28(3): 407-413.
[5]. Piri, M., Mikaeil, R., Hashemolhosseini, H., Baghbanan, A., and M. Ataei, (2021). Study of the effect of drill bits hardness, drilling machine operating parameters, and rock mechanical parameters on noise level in hard rock drilling process. Measurement 167: 108447.
[6]. Doiron, H.H. and Deane, J.D. (1982). Effects of hydraulic parameter cleaning variations on rate of penetration of soft formation insert bits. SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, ISBN: 978-1-55563-666-1.
[7]. Harold, H. and John, D. (1982). Effect of hydraulic parameter cleaning variations on rate of penetration of soft formation insert bit. Paper SPE 11058 Presented at the 57th Annual Fall Technical Conference and Exhibition, New Orleans, CA.
[8]. Ataei, M., KaKaie, R., Ghavidel, M., and Saeidi, O. (2015). Drilling rate prediction of an open-pit mine using the rock mass drillability index. International Journal of Rock Mechanics and Mining Sciences 73: 130-138.
[9]. Irawan, S., Rahman, A., and Tunio, S. (2012). Optimization of weight on bit during drilling operation based on rate of penetration model. Research Journal of Applied Sciences, Engineering and Technology 4(12): 1690-1695.
[10]. Garnier, A. and Van Lingen, N. (1959). Phenomena affecting drilling rates at depth.
[11]. Alipenhani, B., Majdi, A., and Amnieh, H.B. (2022). Capability assessment of rock mass in block caving mining method based on numerical simulation and response surface methodology. Journal of Mining and Environment 13(2): 579-606.
[12]. Taguchi, G. (1987). System of experimental design; engineering methods to optimize quality and minimize costs.
[13]. Gunst, R.F. (1996). Response surface methodology: process and product optimization using designed experiments. Taylor & Francis.
[14]. Millheim, K.K. (1983). An engineering simulator for drilling: part II, SPE Annual Technical Conference and Exhibition. Soc. Pet. Eng. ISBN: 978-1-55563-652-4.
[15]. Montgomery, D.C. and Raymond, H.M. (2002). Response Surface Methodology: Process and Product Optimization using Designed Experiments. John Wiley, New York.
[16]. Kyratsis, P., Markopoulos, A.P., Efkolidis, N., Maliagkas, V., and Kakoulis, K. (2018). Prediction of thrust force and cutting torque in drilling based on the response surface methodology. Machines 6(2): 24.
[17]. Moradi, M. and Mohazabpak, A. R. (2018). Statistical Modelling and Optimization of Laser Percussion Microdrilling of Inconel 718 Sheet using Response Surface Methodology (RSM). Lasers in Engineering (Old City Publishing), 39.
[18]. Salehnezhad, L., Heydari, A., and Fattahi, M. (2019). Experimental investigation and rheological behaviors of water-based drilling mud contained starch-ZnO nanofluids through response surface methodology. Journal of Molecular Liquids 276: 417-430.
[19]. Zhang, W., Huang, Z., Kang, M., Shi, M., Deng, R., Yan, Y., and Zhu, Q. (2021). Research on multivariate nonlinear regression model of specific energy of rock with laser drilling based on response surface methodology. Optics Communications 489: 126865.
[20]. Alakbari, F.S., Mohyaldinn, M.E., Ayoub, M.A., Muhsan, A.S., and Hassan, A. (2021). Apparent and plastic viscosities prediction of water-based drilling fluid using response surface methodology. Colloids and Surfaces A: Physicochemical and Engineering Aspects 616: 126278.
[21]. Surekha, B., Sree Lakshmi, T., Jena, H., and Samal, P. (2021). Response surface modelling and application of fuzzy grey relational analysis to optimise the multi response characteristics of EN-19 machined using powder mixed EDM. Australian Journal of Mechanical Engineering 19(1): 19-29.
[22]. Capik, M. and Batmunkh. B. (2021), Measurement Prediction, and Modeling of Bit Wear During Drilling Operations. Journal of Mining and Environment 12(1): 15-30.
[23]. Modi, M., Agarwal, G., Patil, V., Bhatia, U., and Pancholi, R. (2019). Parametric optimization in drilling of Al–SiC composite using Taguchi method. International Journal of Scientific & Technology Research 8(9): 2019-22.
[24]. Aamir, M., Tu, S., Tolouei-Rad, M., Giasin, K., and Vafadar, A. (2020). Optimization and modeling of process parameters in multi-hole simultaneous drilling using Taguchi method and fuzzy logic approach. Materials 13(3): 680.
[25]. Venkateshwarlu, N., Singaravel, B., Shekar, K.C., and Prasad, S.D. (2021). Analysis and optimization of circularity error in drilling Process using statistical technique. IOP Conference Series: Materials Science and Engineering 1057(1): 012063.
[26]. Cooper, G.A. (2006). Pay zone–drilling simulator– operators manual. Houston, USA.
[27]. Pelfrene, G. (2010). Rôle du processus de forabilité des roches dans les vibrations de torsion des systèmes de forage pétrolier - Khochemane, L., (1990). Augmentation de l’Efficacité Technique d’Utilisation des Machines de Forage Rotatif a Taillant Coupant. Master’s Thesis. Université d’Annaba, Annaba, Algeria.
[28]. Montgomery D.C. (2001). Response surface methods and other approaches to optimization. Design and analysis of experiments. New York: John Wiley & Sons.
[29]. Gaitonde, V., Karnik, S., Achyutha, B., Siddeswarappa, B., and Davim, J.P. (2009). Predicting burr size in drilling of AISI 316L stainless steel using response surface analysis. International Journal of Materials and Product Technology 35(1-2): 228-245.
[30]. Aouici, H., Yallese, M.A., Chaoui, K., Mabrouki, T., and Rigal, J.-F. (2012) Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization. Measurement 45(3): 344-353.
[31]. Bouzid, L., Yallese, M.A., Chaoui, K., Mabrouki, T., and Boulanouar, L. (2015). Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology. The Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 229(1): 45-61.
[32]. Myers, R.H. and Montgomery, D.C. (1995). Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, Inc, New York.
[33]. Deng, L. and Cai, C. (2010). Bridge model updating using response surface method and genetic algorithm. Journal of Bridge Engineering15(5) 553-564.
[34]. Subrata, M., Asish, B., and Pradip, K. (2011). Ni-Cr-Mo cladding on mild steel surface using CO2 laser and process modeling with response surface methodology. International Journal of Engineering Science and Technology 3(8): 6805-6816.
[35]. Suresh, R., Basavarajappa, S., and Samuel, G. (2012). Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool. Measurement 45(7): 1872-1884.
[36]. Khosla, A., Kumar, S., and Aggarwal, K.K. (2006). Identification of strategy parameters for particle swarm optimizer through Taguchi method. Journal of Zhejiang University Science A 7(12): 1989-1994.
[37]. Neşeli, S., Yaldız, S., and Türkeş, E. (2011). Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44(3): 580-587.
[38]. Derdour, F.Z., Kezzar, M., Bennis, O., and Khochmane, L. (2018). The optimization of the operational parameters of a rotary percussive drilling machine using the Taguchi method. World Journal of Engineering 15(1): 62-69.
[39]. Mostefaoui, A., Sari, M.R., Kezzar, M., and Eid, M. R. (2023). Statistical investigation on the failure of misaligned contaminated EHL rolling contacts using response surface methodology. The International Journal of Advanced Manufacturing Technology 127, 1225–1242.
[40]. Khentout, A., Kezzar, M., and Khochemane, L. (2019). Taguchi Optimization And Experimental Investigation Of The Penetration Rate Of Compact Polycrystalline Diamond Drilling Bits In Calcareous Rocks. International Journal of Technology 10(2): 226-235.
[41]. Derdour, F. Z., Kezzar, M., and Khochemane, L. (2018). Optimization of penetration rate in rotary percussive drilling using two techniques: Taguchi analysis and response surface methodology (RMS). Powder Technology 339, 846-853. | ||
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