Volume 20, Issue 4 (April 2020)                   Modares Mechanical Engineering 2020, 20(4): 987-997 | Back to browse issues page

1- Mechanical Engineering Department, Mechanical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran
2- Mechanical Engineering Department, Mechanical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran , m.sheikhi@sru.ac.ir
3- Mechanical Engineering Department, Mechanical Engineering Faculty, Arak University, Arak, Iran
Abstract:   (2349 Views)
Cortical bone milling is used in orthopedic surgeries such as knee replacement, otological, spinal cord, and hip replacement. The cutting forces created by the cutting tool during cortical bone milling in order to control the wear of the tool as well as applying allowable force to the bone to prevent fracture should be controlled. In this paper, the effective parameters in the bone milling including cutting speed, feed rate, cutting depth and tool diameter has been investigated using the response surface method in order to predict the cutting forces. In this method, a second-order linear regression equation can be presented in order to predict the behavior of the bone milling process precisely. Also, Sobel's sensitivity analysis method was used to study the effect of input parameters on the behavior of cutting force. In this research, the behavior of different input parameters and the effect of their interactions on the machining force has been evaluated and analyzed. The components of cutting force were measured and investigated in three directions of feed, perpendicular to the feed and perpendicular to the bone surface. The results show that the mathematical model governing the problem has a proper function within the range of the defined parameters and it can provide a good prediction of force behavior. The minimum cutting force can be achieved in a rotational speed of 1500 rpm, feed of 12 mm/min, tool diameter of 2 mm, and cutting depth of 0.2 mm. Also, about the sensitivity of the force behavior based on the input parameters variation in the range of experiments, the greatest effect was related to the cutting depth with 36.3% of the effect, and feed rate with 28.4% of the effect, the diameter of the tool with 27.5% of the effect and the rotational speed with 7.8% of the effect.
Article Type: Original Research | Subject: Biomechanics
Received: 2019/05/12 | Accepted: 2019/08/4 | Published: 2019/04/18

References
1. American Academy of Orthopaedic Surgeons. Total knee replacement [Internet]. Rosemont: American Academy of Orthopaedic Surgeons; 2015 [Unknown Cited]. Available from: https://orthoinfo.aaos.org/en/treatment/total-knee-replacement/ [Link]
2. James TP, Chang G, Micucci S, Sagar A, Smith EL, Cassidy C. Effect of applied force and blade speed on histopathology of bone during resection by sagittal saw. Medical Engineering & Physics. 2014;36(3):364-370. [Link] [DOI:10.1016/j.medengphy.2013.12.002]
3. Fox MJ, Scarvell JM, Smith PN, Kalyanasundaram S, Stachurski ZH. Lateral drill holes decrease strength of the femur: An observational study using finite element and experimental analyses. Journal of Orthopaedic Surgery and Research. 2013;8:29. [Link] [DOI:10.1186/1749-799X-8-29]
4. Tai BL, Zhang L, Wang A, Sullivan S, Shih AJ. Neurosurgical bone grinding temperature monitoring. Procedia CIRP. 2013;5:226-230. [Link] [DOI:10.1016/j.procir.2013.01.045]
5. Marco M, Rodríguez-Millán M, Santiuste C, Giner E, Henar Miguélez M. A review on recent advances in numerical modelling of bone cutting. Journal of the Mechanical Behavior of Biomedical Materials. 2015;44:179-201. [Link] [DOI:10.1016/j.jmbbm.2014.12.006]
6. Cao T, Li X, Gao Z, Feng G, Shen P. A method for identifying otological drill milling through bone tissue wall. The International Journal of Medical Robotics. 2011;7(2):148-155. [Link] [DOI:10.1002/rcs.382]
7. Lonner JH. Robotically assisted unicompartmental knee arthroplasty with a handheld image-free sculpting tool. Operative Techniques in Orthopaedics. 2015;25(2):104-113. [Link] [DOI:10.1053/j.oto.2015.03.001]
8. Natali C, Ingle P, Dowell J. Orthopaedic bone drills-can they be improved? Temperature changes near the drilling face. The Journal of Bone and Joint Surgery British Volume. 1996;78(3):357-362. [Link] [DOI:10.1302/0301-620X.78B3.0780357]
9. Pandey RK, Panda SS. Drilling of bone: A comprehensive review. Journal of Clinical Orthopaedics and Trauma. 2013;4(1):15-30. [Link] [DOI:10.1016/j.jcot.2013.01.002]
10. Denis K, Van Ham G, Vander Sloten J, Van Audekercke R, Van der Perre G, De Schutter J, et al. Influence of bone milling parameters on the temperature rise, milling forces and surface flatness in view of robot-assisted total knee arthroplasty. International Congress Series. 2001;1230:300-306. [Link] [DOI:10.1016/S0531-5131(01)00067-X]
11. Wang W, Shi Y, Yang N, Yuan X. Experimental analysis of drilling process in cortical bone. Medical Engineering & Physics. 2014;36(2):261-266. [Link] [DOI:10.1016/j.medengphy.2013.08.006]
12. Arbabtafti M, Moghaddam M, Nahvi A, Mahvash M, Richardson B, Shirinzadeh B. Physics-based haptic simulation of bone machining. IEEE Transactions on Haptics. 2011;4(1):39-50. [Link] [DOI:10.1109/TOH.2010.5]
13. Moghaddam M, Nahvi A, Arbabtafti M, Mahvash M. A physically realistic voxel-based method for haptic simulation of bone machining. In: Ferre M, editor. EuroHaptics 2008: Haptics: Perception, Devices and Scenarios, Lecture Notes in Computer Science, vol 5024. Heidelberg: Springer; 2008. [Link]
14. Kianmajd B, Carter D, Soshi M. A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty. International Journal of Computer Assisted Radiology and Surgery. 2016;11:1871-1880. [Link] [DOI:10.1007/s11548-016-1355-x]
15. Plaskos C. Modeling and design of robotized tools and milling techniques for total knee arthroplasty [Dissertation]. Grenoble: Université Joseph-Fourier; 2005. [Link]
16. Wu D, Zhang L, Liu S. Research on establishment and validation of cutting force prediction model for bone milling. IEEE International Conference on Robotics and Biomimetics (ROBIO), 6-9 December 2015, Zhuhai, China. Piscataway: IEEE; 2016. [Link] [DOI:10.1109/ROBIO.2015.7419044]
17. Van Ham G, Denis K, Vander Sloten J, Van Audekercke R, Van der Perre G, De Schutter J, et al. Machining and accuracy studies for a tibial knee implant using a force-controlled robot. Computer Aided Surgery. 1998;3(3):123-133. [Link] [DOI:10.3109/10929089809149840]
18. Inoue T, Sugita N, Mitsuishi M, Saito T, Nakajima Y, Yokoyama Y, et al. Optimal control of cutting feed rate in the robotic milling for total knee arthroplasty. 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, 26-29 Sept. 2010, Tokyo, Japan. Piscataway: IEEE; 2010. [Link] [DOI:10.1109/BIOROB.2010.5626940]
19. Federspil PA, Plinkert B, Plinkert PK. Experimental robotic milling in skull-base surgery. Computer Aided Surgery. 2003;8(1):42-48. [Link] [DOI:10.3109/10929080309146102]
20. Sugita N, Genma F, Nakajima Y, Mitsuishi M. Adaptive controlled milling robot for orthopedic surgery. IEEE International Conference on Robotics and Automation, 10-14 April 2007, Roma, Italy. Piscataway: IEEE; 2007. [Link] [DOI:10.1109/ROBOT.2007.363053]
21. Plaskos C, Hodgson AJ, Cinquin P. Modelling and optimization of bone-cutting forces in orthopaedic surgery. In: Ellis RE, Peters TM, editors. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003, MICCAI 2003. Lecture Notes in Computer Science, vol 2878. Heidelberg: Springer; 2003. 254-261. [Link] [DOI:10.1007/978-3-540-39899-8_32]
22. Shakouri E, Sadeghi M, Maerefat M. Experimental investigation of thermal necrosis in conventional and high speed drilling of bone. Microbial Drug Resistance Journal. 2013;13(10):105-117. [Link]
23. Plaskos C. Bone sawing and milling in computer-assisted total knee arthroplasty [Dissertation]. London: University of Western Ontario; 1999. [Link]
24. Hu Y, Jin H, Zhang L, Zhang P, Zhang J. State recognition of pedicle drilling with force sensing in a robotic spinal surgical system. IEEE/ASME Transactions on Mechatronics. 2014;19(1):357-365. [Link] [DOI:10.1109/TMECH.2012.2237179]
25. Dai Y, Xue Y, Zhang J. Vibration-based milling condition monitoring in robot-assisted spine surgery. IEEE/ASME Transactions on Mechatronics. 2015;20(6):3028-3039. [Link] [DOI:10.1109/TMECH.2015.2414177]
26. Deng Z, Jin H, Hu Y, He Y, Zhang P, Tian W, et al. Fuzzy force control and state detection in vertebral lamina milling. Mechatronics. 2016;35:1-10. [Link] [DOI:10.1016/j.mechatronics.2016.02.004]
27. Jin H, Hu Y, Deng Z, Zhang P, Song Z, Zhang J. Model-based state recognition of bone drilling with robotic orthopedic surgery system. IEEE International Conference on Robotics and Automation (ICRA), 31 May-7 June 2014, Hong Kong, China. Piscataway: IEEE; 2014. [Link] [DOI:10.1109/ICRA.2014.6907369]
28. Alam K, Mitrofanov AV, Silberschmidt VV. Experimental investigations of forces and torque in conventional and ultrasonically-assisted drilling of cortical bone. Medical Engineering & Physics. 2011;33(2):234-239. [Link] [DOI:10.1016/j.medengphy.2010.10.003]
29. Augustin G, Davila S, Mihoci K, Udiljak T, Vedrina DS, Antabak A. Thermal osteonecrosis and bone drilling parameters revisited. Archives of Orthopaedic and Trauma Surgery. 2008;128(1):71-77. [Link] [DOI:10.1007/s00402-007-0427-3]
30. Pandey RK, Panda SS. Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach. Journal of Intelligent Manufacturing. 2015;26:1121-1129. [Link] [DOI:10.1007/s10845-013-0844-9]
31. Ghoreishi M, Tahmasbi V. Optimization of material removal rate in dry electro-discharge machining process 2. Modares Mechanical Engineering. 2015;14(12):113-121. [Persian] [Link]
32. Hou TH, Su CH, Liu WL. Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm. Powder Technology. 2007;173(3):153-162. [Link] [DOI:10.1016/j.powtec.2006.11.019]
33. Montgomery DC. Design and analysis of experiments. Hoboken: John Wiley & Sons; 2008. [Link]
34. Nekahi A, Dehghani K. Modeling the thermomechanical effects on baking behavior of low carbon steels using response surface methodology. Materials & Design. 2010;31(8):3845-3851. [Link] [DOI:10.1016/j.matdes.2010.03.038]
35. Moradi M, Ghoreishi M, Frostevarg J, Kaplan AFH. An investigation on stability of laser hybrid arc welding. Optics and Lasers in Engineering. 2013;51(4):481-487. [Link] [DOI:10.1016/j.optlaseng.2012.10.016]
36. Korayem M, Rastegar Z, Taheri M. Sensitivity analysis of nano-contact mechanics models in manipulation of biological Cell. Nanoscience and Nanotechnology. 2012;2(3):49-56. [Link] [DOI:10.5923/j.nn.20120203.02]
37. Dillon NP, Kratchman LB, Dietrich MS, Labadie RF, Webster RJ, Withrow TJ. An experimental evaluation of the force requirements for robotic mastoidectomy. Otology and Neurotology. 2013;34(7):e93-e102. [Link] [DOI:10.1097/MAO.0b013e318291c76b]
38. Shakouri E, Sadeghi MH, Maerefat M, Karafi MR, Memarpour M. Experimental and analytical investigation of thrust force in ultrasonic assisted drilling of bone. Modares Mechanical Engineering. 2014;14(6):194-200. [Persian] [Link]
39. Yeager C, Nazari A, Arola D. Machining of cortical bone: Surface texture, surface integrity and cutting forces. Machining Science and Technology. 2008;12(1):100-118. [Link] [DOI:10.1080/10910340801890961]
40. Sugita N, Ishii K, Sui J, Terashima M. Multi-grooved cutting tool to reduce cutting force and temperature during bone machining. CIRP Annals. 2014;63(1):101-104. [Link] [DOI:10.1016/j.cirp.2014.03.069]