Abstract
For the improvement of economic and environmental performance of bone drilling operations, the optimum value of the spindle speed and feed rate must be known. This is because the input spindle speed is an important factor of energy consumption and with its approximate value known in optimization of output characteristics of bone drilling operations may result in significant energy savings. Optimization of multi-output characteristics of the bone drilling process is possible, if the relationships between the outputs and the inputs are known. Therefore, this study forms the strong basis for development of the models for the three output characteristics (maximum temperature, maximum force and maximum average surface roughness) for the bone drilling operation performed on the bovine bone. Experimental studies are conducted to measure these three outputs based on the spindle speed and feed rate. The validation of the formulated models is done based on the root-mean-square error, coefficient of determination, relative error, multi-objective error and mean absolute percentage error. The relationships between the three outputs and the inputs are further revealed by the 2-D analysis on the models. The findings from these relationships can be used for the predictive monitoring the bone drilling operation. The work concludes with discussion of environmental implications arising from the current study.
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Kuram E, Ozcelik B, Bayramoglu M, Demirbas E, Simsek BT (2013) Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. J Clean Prod 42:159–166
Garg A, Bhalerao Y, Tai K (2013) Review of empirical modelling techniques for modelling of turning process. Int J Model Identif Control 20:121–129
Garg A, Tai K (2012) Review of genetic programming in modeling of machining processes. Modelling, identification & control (Icmic). In: 2012 Proceedings of international conference on Ieee. pp 653–658
Mukherjee I, Ray PK (2006) A Review of optimization techniques in metal cutting processes. Comput Ind Eng 50:15–34
Chandrasekaran M, Muralidhar M, Krishna CM, Dixit U (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46:445–464
Udiljak T, Ciglar D, Skoric S (2007) Investigation into bone drilling and thermal bone necrosis. Adv Prod Eng Manag 2:103–112
Hillery MT, Shuaib I (1999) Temperature effects in drilling of human and bovine bone. J Mater Process Technol 92–93:302–308
Lee J, Ozdoganlar OB, Rabin Y (2012) An experimental investigation on thermal exposure during bone drilling. Med Eng Phys 34(10):1510–1520
Karaca F, Aksakal B, Kom M (2011) Influence of orthopaedic drilling parameters on temperature and histopathology of bovine tibia: an in vitro study. Med Eng Phys 33(10):221–227
Augustin G, Davila S, Mihoci K, Udiljak T, Vedrina DS, Antabak A (2008) Thermal osteonecrosis and bone drilling parameters revisited. Arch Orthop Trauma Surg 128:71–77
Lundskog J (1972) Heat and bone tissue, scand. J Plast Reconstr Surg Suppl 9:1–80
Deng J (1989) Introduction to grey system. J. Grey Syst 1(1):1–24
Pandey RK, Panda SS (2013) Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach. J Intell Manuf. doi:10.1007/s10845-013-0844-9
Pandey RK, Panda SS (2014) Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement 47:386–392
Pandey RK, Panda SS (2014) A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology. Proc Inst Mech Eng H J Eng Med 228:1135–1145
Pandey RK, Panda SS (2015) Multi-performance optimization of bone drilling using Taguchi method based on membership function. Measurement 59:9–13
Bhushan RK (2013) Optimization of cutting parameters for minimizing energy consumption and maximizing tool life during machining of al alloy sic particle composites. J Clean Prod 39:242–254
Kant G, Sangwan KS (2014) Prediction and optimization of machining parameters for minimizing energy consumption and surface roughness in machining. J Clean Prod 83:151–164
Alam K, Mitrofanov AV, Silberschmidt VV (2009) Measurements of surface roughness in conventional and ultrasonically assisted bone drilling. Am J Biomed Sci 1(4):312–320
Panda BN, Garg A, Shankhwar K (2016) Empirical investigation of environmental characteristic of 3-D additive manufacturing process based on slice thickness and part orientation. Measurement 86:293–300
Garg A, Panda B, Shankhwar K (2016) Investigation of the joint length of weldment of environmental-friendly magnetic pulse welding process. Int J Adv Manuf Technol. doi:10.1007/s00170-016-8634-0
Panda BN, Bahubalendruni MR, Biswal BB (2014) Comparative evaluation of optimization algorithms at training of genetic programming for tensile strength prediction of FDM processed part. Procedia Materials Science 5:2250–2257
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112
Garg A, Lam JSL, Gao L (2015) Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. J Clean Prod 108:34–45
Panda B, Garg A, Jian Z, Heidarzadeh A, Gao L (2016) Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed. Front Mech Eng. doi:10.1007/s11465-016-0393
Garg A, Panda BN, Zhao DY, Tai K (2016) Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell. Chemometr Intell Lab Syst 155:7–18
Acknowledgements
This study was supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002). This project is also supported by National Natural Science Foundation of China (61502291), the Cultivation Project for Outstanding Young Teachers in Higher Education Institutions of Guangdong Province (YQ2015070) and the Characteristic Innovation Project in Higher Education Institutions of Guangdong Province (2015GXJK037, 2015KTSCX039).
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Garg, A., Shankhwar, K., Jiang, D. et al. An evolutionary framework in modelling of multi-output characteristics of the bone drilling process. Neural Comput & Applic 29, 1233–1241 (2018). https://doi.org/10.1007/s00521-016-2632-x
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DOI: https://doi.org/10.1007/s00521-016-2632-x