Experimental analysis, mathematical modeling and Sobol sensitivity analysis of surface roughness in orthopedic milling process (polymethylmethacrylate)

Authors

1 Mechanical Engineering Department, Mechanical Engineering Faculty, shahid rajaee Teacher training university, Tehran, Iran

2 Department Of Mechanical Engineering, Arak University Of Technology , Arak, Iran

3 Mechanical Engineering, Department , Mechanical Engineering Faculty, Arak university , Arak, Iran

Abstract

One of the common treatments in orthopedic surgery is machining of knee joints to attach the artificial joint. In this surgery preparation of tibia and femur surfaces is necessary to obtain proper joint kinematics and ligament balancing. For this purpose milling is involved in surgery requiring accurate machining of bone surfaces and creation of accurate slots. In this paper, milling process was carried out on polymethylmethacrylate as workpiece and suitable substitute for bone and for the first time, it is focused on modeling and optimizing the effective parameters in milling namely cutting speed, feed rate, tool diameter and cutting depth for analyzing the surface roughness using the surface response methodology. The second-order regression equation governing the model is drived and the influence of the input parameters and their interaction on the surface roughness as the output parameter is carefully investigated. Also, sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters quantitatively. The results show that in order to achieve the desirable surface quality, minimum of feed, minimum rotational speed, smaller tool diameter and low cutting depth should be considered. Results show that among all effective input parameters tool rotational speed, feed rate, tool diameter and ctting depth have the highest influence on process roughness respectively. The behavior of each output parameters with variation in each input parameter is further investigated.

Keywords

References

[1] Marco  M, Rodríguez-Millán M, Santiuste C, Giner E, Miguélez MH (2015) A review on recent advances in numerical modelling of bone cutting. J Mech Behav Biomed Mater 44: 179-201.
[2] Fadda M, Marcacci M, Toksvig-Larsen S, Wang T,     Meneghello R (1998) Improving accuracy of bone resections using robotics tool holder and a high speed milling cutting tool. J Med Eng Technol 22(6): 280-284.
[3] Denis K, Van Ham G, Vander Sloten J, Van Audekercke R, Van der Perre G, De Schutter J, Kruth JP, Bellemans J, Fabry G (2001) Influence of bone milling parameters on the temperature rise, milling forces and surface flatness in view of robot-assisted total knee arthroplasty. Intl Congress Series 1230: 300-306.
[4] Van Ham G, Denis K, Vander Sloten J, Van Audekercke R, Van der Perre G, De Schutter J, Aertbelien E, Demey S, Bellemans J (1998) Machining and accuracy studies for a tibial knee implant using a force-controlled robot. Comput Aided Surg 3(3): 123-133
[5] Singh G, Jain V, Gupta D, Ghai A (2016) Optimization of process parameters for drilled hole quality characteristics during cortical bone drilling using Taguchi method. J Mech Behav Biomed Mater 62: 355-365.
[6] Tahmasbi V, Ghoreishi M, Zolfaghari M, (2015) Modeling and multi objective optimization of effective parameters in drilling cortical bone. Mod Mech Eng 14(13): 113-119.
[7] Alam K, A Mitrofanov V, Silberschmidt VV, (2009) Measurements of Surface Roughness in Conventional and Ultrasonically Assisted Bone Drilling. Am J biomed Sci 1: 9.
[8] Singh G, Jain V, Gupta D (2015) Comparative study for surface topography of bone drilling using conventional drilling and loose abrasive machining. Proc Inst Mech Eng H 229(3): 225-231.
[9] Yeager C, Nazari A, Arola D (2008) Machining of cortical bone: Surface texture, surface integrity and cutting forces. Mach Sci Technol 12(1): 100-118.
[10] Toksvig-Larsen S, Ryd L (1991) Surface flatness after bone cutting. A cadaver study of tibial condyles. Acta Orthop Scand 62(1): 15-18.
[11] Aspenberg P, Goodman S, Toksvig-Larsen S, Ryd L, Albrektsson T (1992) Intermittent micromotion inhibits bone ingrowth. Titanium implants in rabbits. Acta Orthop Scand 63(2): 141-5.
[12] Friedman RJ (1992) Advances in biomaterials and factors affecting implant fixation. Instr Course Lect 41: 127-136.
[13] Wu LD, Hahne HJ, Hassenpflug J (2004) The dimensional accuracy of preparation of femoral cavity in cementless total hip arthroplasty. J Zhejiang Univ Sci 5(10): 1270-1278.
[14] Dahotre N, Joshi S (2016) Machining of  bone and hard tissues. Springer International Publishing.
[15] Plaskos C (1999) Bone sawing and milling in computer-assisted total knee arthroplasty. Theses,  univ of western ontario.
[16] Pandey RK, Panda SS (2013) Predicting temperature in orthopaedic drilling using back propagation neural network. Procedia Eng 51: 676-682.
[17] Lee J, Ozdoganlar OB, Rabin Y (2012) An experimental investigation on thermal exposure during bone drilling. Med Eng Phys 34(10): 1510-1520.
[18] Kuppuswamy R, Christie-Taylor B (2019) Influence of surgical drill geometry on drilling performance of cortical and trabecular bone. in Proc of Springer Singapore 119-131.
[19] Charnley J (1960) Anchorage of the femoral head prosthesis to the shaft of the femur. J Bone Joint Surg Br 42-b: 28-30.
[20] Kalidindi V (2004) Optimization of drill design and coolant systems during dental implant surgery.  Thesis, Univ of Kentucky.
[21] Whitehouse D (2004) Surfaces and their Measurement. 1st edn. Butterworth-Heinemann.
[22] Myers RH, Montgomery DC, Vining GG, Borror CM, Kowalski SM (2004) Response surface methodology: A retrospective and literature survey. J Qual Technol 36(1): 53-77.
[23] Montgomery DC (2008) Design and analysis of experiments. John  Wiley & Sons.
[24] Sobol′IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55(1): 271-280.
[25] Kalidindi V (2004) Optimization of drill design and coolant systems during dental implant surgery.  MS Thesis, Kentucky.
[26] Saha S, Pal S (1984) Mechanical properties of bone cement: a review. J Biomed Mater Res 18(4): 435-462.
[27] Joseph Davidson M, Balasubramanian K, Tagore GRN (2008) Surface roughness prediction of flow-formed AA6061 alloy by design of experiments. J Mater Process Technol 202(1): 41-46.
[28] Carlsson L, Rostlund T, Albrektsson B, Albrektsson T (1988) Implant fixation improved by close fit. Cylindrical implant-bone interface studied in rabbits. Acta Orthop Scand 59(3): 272-275.
[29] Daugaard H, Elmengaard B, Bechtold JE,      Jensen T, Soballe K (2010) The effect on          bone growth enhancement of implant coatings   with hydroxyapatite and collagen deposited electrochemically and by plasma spray. J Biomed Mater Res 92(3): 913-921.
[30] Altintas Y, Ber A (2001) Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design. APPL MECH REV 54(5).
[31] Saffar RJ, Razfar MR, Salimi AH, Khani MM (2009) Optimization  of machining parameters to minimize tool deflection in the end milling operation using genetic algorithm. World Appl Sci 6(1): 64-69.
[32] Wang W, Kweon SH, Yang SH, (2005) A study on roughness of the micro-end-milled surface produced by a miniatured machine tool. J Mater Process Technol 162-163: 702-708.
[33] Christie NR (2007) Fundamentals of machining and machine tools. 2nd edn. Int J Prod Res  28(1): 215-215.
[34] Thepsonthi T, Özel T (2015) 3-D finite element process simulation of micro-end milling Ti-6Al-4V titanium alloy: Experimental validations on chip flow and tool wear. J Mater Process Technol 221: 128-145.
Journal of Solid and Fluid Mechanics
Volume 11, Issue 1
March and April 2021
Pages 139-152

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