| تعداد نشریات | 24 |
| تعداد شمارهها | 851 |
| تعداد مقالات | 7,575 |
| تعداد مشاهده مقاله | 13,413,626 |
| تعداد دریافت فایل اصل مقاله | 11,580,408 |
Multi-objective Pareto optimization of bone drilling process using NSGA II algorithm | ||
| Iranian Journal of Materials Forming | ||
| مقاله 5، دوره 5، شماره 2، دی 2018، صفحه 72-83 اصل مقاله (1.05 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/ijmf.2018.29391.1099 | ||
| نویسندگان | ||
| V. Tahmasbi* 1؛ H. Safikhani2؛ F. Setoudeh3 | ||
| 1Department of Mechanical Engineering, Arak University of Technology, Arak, Iran | ||
| 2Department of Mechanical Engineering, Arak University, Arak, Iran | ||
| 3Department of Electrical Engineering, Arak University of Technology, Arak, Iran | ||
| چکیده | ||
| Bone drilling process is one the most common processes in orthopedic surgeries and bone breakages treatment. It is also very frequent in dentistry and bone sampling operations. Bone is a complex material and the machining process itself is sensitive so bone drilling is one of the most important, common and sensitive processes in Biomedical Engineering field. Orthopedic surgeries can be improved using robotic bone drilling systems and mechatronic bone drilling tools. In the present study, multi-objective optimization is performed on the temperature and trust force at two steps. At the first step, two regression models are developed for modeling the temperature and force in bone drilling process considering three design variables namely tool’s rotational speed (V), feed rate (f) and tool diameter (D). At the second step, by using regression models, multi-objective genetic algorithm is used for Pareto based optimization of bone drilling process considering two conflicting objectives: temperature and force. It has been found out that there are considerable connections and feasible principles for an optimal design of the process in case of applying Pareto-based multi-objective optimization; otherwise these interesting results would not be discernible. | ||
| کلیدواژهها | ||
| Pareto optimization؛ bone drilling؛ temperature؛ thermal necrosis؛ NSGA II | ||
| مراجع | ||
|
[1] W. Wang, Y. Shi, N. Yang, X. Yuan, Experimental analysis of drilling process in cortical bone, Medical engineering & physics 36(2) (2014) 261-266.
[2] R.K. Pandey, S. Panda, Drilling of bone: A comprehensive review, Journal of clinical Orthopaedics and Trauma 4(1) (2013) 15-30.
[3] M. Louredo, I. Díaz, J.J. Gil, DRIBON: A mechatronic bone drilling tool. Mechatronics 22(8) (2012) 1060-1066.
[4] M.H. Aziz, M.A. Ayub, R. Jaafar, Real-time algorithm for detection of breakthrough bone drilling, Procedia Engineering 41 (2012) 352-359.
[5] I. Díaz, J.J. Gil, M. Louredo, Bone drilling methodology and tool based on position measurements, Computer methods and programs in biomedicine 112(2) (2013) 284-292.
[6] J. Sui, N. Sugita, K. Ishii, K. Harada, Mechanistic modeling of bone-drilling process with experimental validation Journal of Materials Processing Technology 214(4) (2014) 1018-1026.
[7] G. Augustin, T. Zigman, S. Davila, T. Udilljak, Cortical bone drilling and thermal osteonecrosis, Clinical biomechanics 27(4) 2012 313-325.
[8] K.N. Bachus, M.T. Rondina, D.T. Hutchinson, The effects of drilling force on cortical temperatures and their duration: an in vitro study, Medical engineering & physics 22(10) (2000) 685-691.
[9] P. FG, Histological change in bone after insertion of skeletal fixation pins, J Oral Surg Anest Hosp Dent Serv 18 (1960) 9.
[10] F. Bronner, M.C. Farach-Carson, J. Rubin, S.D. Bain, Bone Resorption, (2005): Springer.
[11] A. Eriksson, T. Albrektsson, Temperature threshold levels for heat-induced bone tissue injury: a vital-microscopic study in the rabbit, The Journal of prosthetic dentistry 50(1) (1983) 101-107.
[12] A.R. Moritz, F. Henriques Jr, Studies of thermal injury: II. The relative importance of time and surface temperature in the causation of cutaneous burns, The American journal of pathology 23(5) (1947) p. 695.
[13] R. Eriksson, T. Albrektsson, The effect of heat on bone regeneration: an experimental study in the rabbit using the bone growth chamber, Journal of Oral and Maxillofacial surgery 42(11) (1984) 705-711.
[14] Y. Hou, C. Li, H. Ma, Y. Zhang, M. Yang, X. Zhang, A Theoretical Analysis on Bone Drilling Temperature Field of Superhard Drill, The Open Mechanical Engineering Journal 10(1) (2016).
[15] L. Lamazza, G. Garreffa, D. Laurito, M. Lollobrigida, L. Palmieri, et al., Temperature Values Variability in Piezoelectric Implant Site Preparation: Differences between Cortical and Corticocancellous Bovine Bone, BioMed research international (2016) 2016.
[16] R.K. Pandey, S.S. Panda, Optimization of multiple quality characteristics in bone drilling using grey relational analysis, Journal of orthopaedics 12(1) (2015) 39-45.
[17] J. Lundskog, Heat and bone tissue. An experimental investigation of the thermal properties of bone and threshold levels for thermal injury, Scandinavian journal of plastic and reconstructive surgery 9 (1971) 1-80.
[18] K. Deb, S. Karthik. Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, International conference on evolutionary multi-criterion optimization (2007). Springer.
[19] H. Safikhani, A. Hajiloo, M.J.C. Ranjbar, Modeling and multi-objective optimization of cyclone separators using CFD and genetic algorithms, Computers & Chemical Engineering 35(6) (2011) 1064-1071.
[20] S. Eiamsa-Ard, C. Nuntadusit, P.J.H.T.E. Promvonge, Effect of twin delta-winged twisted-tape on thermal performance of heat exchanger tube, 34(15) (2013) 1278-1288.
[21] H. Safikhani, A. Abbassi, A. Khalkhali, Multi-objective optimization of nanofluid flow in flat tubes using CFD, Artificial Neural Networks and genetic algorithms 25(5) (2014) 1608-1617.
[22] R.K. Pandey, S.J.M. Panda, Optimization of bone drilling parameters using grey-based fuzzy algorithm, Heat Transfer Engineering. 47 (2014) 386-392.
[23] K. Alam, A. Mitrofanov, V.V.J.M.e. Silberschmidt, Experimental investigations of forces and torque in conventional and ultrasonically-assisted drilling of cortical bone, Medical engineering & physics 33(2) (2011) 234-239.
[24] M. Basiaga, Z. Paszenda, J. Szewczenko, Numerical and experimental analyses of drills used in osteosynthesis. Acta of Bioengineering and Biomechanics13(4) (2011) 29-36.
[25] C. Jacobs, M. Pope, J. Berry, F.J.J.o.B. Hoaglund, A study of the bone machining process—orthogonal cutting, Journal of Biomechanics 7(2) (1974) 131-136.
[26] J. Lee, B.A. Gozen, O.B.J.J.o.b. Ozdoganlar, Modeling and experimentation of bone drilling forces, Journal of Biomechanics 45(6) (2012) 1076-1083.
[27] T. Udiljak, D. Ciglar, S.J.A.i.P.E. Skoric, Investigation into bone drilling and thermal bone necrosis, Advances in Production Engineering & Management 2(3) (2007) 103-112.
[28] R. Eriksson, T.J.J.o.O. Albrektsson, M. Surgery, The effect of heat on bone regeneration: an experimental study in the rabbit using the bone growth chamber, Journal of Prosthetic Dentistry 42(11) (1984) 705-711.
[29] R. Vaughn, F. Peyton, The influence of rotational speed on temperature rise during cavity preparation, Journal of dental research 30(5) (1951) 737-744.
[30] G. Augustin, S. Davila, K. Mihoci, T. Udiljak, D.S. Vedrina, Thermal osteonecrosis and bone drilling parameters revisited, Archives of Orthopaedic and Trauma Surgery 128(1) (2008) 71-77.
[31] G. Augustin, S. Davila, T. Udilljak, T. Staroveski, D. Brezak Temperature changes during cortical bone drilling with a newly designed step drill and an internally cooled drill, International Orthopaedics 36(7) (2012) 1449-1456.
[32] F. Karaca, B. Aksakal, M.J.M.E. Kom, Physics, Influence of orthopaedic drilling parameters on temperature and histopathology of bovine tibia: an in vitro study, Medical Engineering & Physics 33(10) (2011) 1221-1227.
[33] J. Lee, O.B. Ozdoganlar, Y.J.M.e. Rabin, physics, An experimental investigation on thermal exposure during bone drilling, Medical Engineering & Physics 34(10) (2012) 1510-1520.
[34] R.K. Pandey, S.J.M. Panda, Multi-performance optimization of bone drilling using Taguchi method based on membership function, measurement 59 (2015) 9-13.
[35] L.S. Matthews, C.J.J. Hirsch, Temperatures measured in human cortical bone when drilling, Journal of bone and joint surgery 54(2) (1972) 297-308.
[36] M. Sharawy, C.E. Misch, N. Weller, S.J.J.o.O. Tehemar, Heat generation during implant drilling: the significance of motor speed, Part H: Journal of Engineering in Medicine 60(10) 2002 1160-1169.
[37] E. Shakouri, M.H. Sadeghi, M. Maerefat, Experimental and analytical investigation of the thermal necrosis in high-speed drilling of bone, Modares Mechanical Engineering 228(4) (2014) 330-341.
[38] G. Augustin, S. Davila, K. Mihoci, T. Udiljak, D.S. Vedrina, A. Antabak, Thermal osteonecrosis and bone drilling parameters revisited, Archives of Orthopaedic and Trauma Surgery 128(1) (2008) 71-77.
[39] K. Alam, Experimental and numerical analysis of conventional and ultrasonically-assisted cutting of bone (2009), © Khurshid Alam.
[40] R.K. Pandey, S. Panda, Optimization of bone drilling parameters using grey-based fuzzy algorithm, Measurement 47 (2014) 386-392.
[41] R.K. Pandey, S. Panda, Multi-performance optimization of bone drilling using Taguchi method based on membership function, Measurement 59 (2015) 9-13.
[42] R.K. Pandey, S. Panda, Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach, Journal of Intelligent Manufacturing 26(6) (2015) 1121-1129.
[43] V. Tahmasbi, M. Ghoreishi, M.J.P.o.t.I.o.M.E. Zolfaghari, Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone, Part H: Journal of Engineering in Medicine 231(11) (2017) 1012-1024.
[44] V. Tahmasbi, M. Ghoreshi, M.J.I.J.o.E.-T.A.B. Zolfaghari, Temperature in bone drilling process: Mathematical modeling and Optimization of effective parameters, 29(7) (2016) 946-953.
[45] V. Tahmasbi, M. Ghoreishi, M.J.B. Zolfaghari, Sensitivity analysis of temperature and force in robotic bone drilling process using Sobol statistical method, Biotechnology & Biotechnological Equipment 32(1) (20181) 30-141.
[46] H. Heydari, M. Zolfaghari, M. Ghoreishi, V. Tahmasbi, Analytical and experimental study of effective parameters on process temperature during cortical bone drilling, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 232(11) (2018) 230-245.
[47] T. Staroveski, D. Brezak, T. Udiljak, Drill wear monitoring in cortical bone drilling, Medical engineering & physics 37(6) (2015) 560-566.
[48] V. Tahmasbi, M. Ghoreshi, M. Zolfaghari, Temperature in bone drilling process: Mathematical modeling and optimization of effective parameters (Technical Note), International Journal of Engineering-Transactions A: Basics 29(7) (2016) 946.
[49] M. Ghoreishi, V. Tahmasbi, Optimization of material removal rate in dry electro-discharge machining process,Modares Mechanical Engineering 14(12) (2015) 9.
[50] D.C. Montgomery, Design and analysis of experiments. (2008) John Wiley & Sons.
[51] T.H. Hou, C. H. Su, W. L. Liu, Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm, Powder Technology 173(3) (2007) 153-62.
[52] R.H. Myers, D.C. Montgomery, C.M.J.A.P. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics). (1995).
[53] S.M. Assarzadeh, Ghoreishi, A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining (PMEDM) parameters, The International Journal of Advanced Manufacturing Technology 64(9-12) (2013) 1459-1477.
[54] H. Safikhani, A. Hajiloo, M. Ranjbar, Modeling and multi-objective optimization of cyclone separators using CFD and genetic algorithms, Computers & Chemical Engineering 35(6) (2011) 1064-1071.
[55] H. Safikhani, A. Abbassi, A. Khalkhali, M. Kalteh, Multi-objective optimization of nanofluid flow in flat tubes using CFD, artificial neural networks and genetic algorithms, Adv. Powder Technol 25(5) (2014) 1608-1617.
| ||
|
آمار تعداد مشاهده مقاله: 1,087 تعداد دریافت فایل اصل مقاله: 776 |
||