Bone drilling is a mechanical, thermal coupling process utilized in orthopedics for provision of rigid internal fixation and treatment of fractured bone. Rotary ultrasonic-assisted bone drilling (RUABD) has achieved noteworthy interest in orthopedic practice due to its ability to enhance biomechanical pullout strength. Drilling parameters used during orthopedic surgeries significantly impact the holding power, initial implant stability and pullout strength. It is difficult for surgeons to predict push-out strength at the interface of bone and screw. An intelligent approach could involve utilizing machine learning (ML) to train and test independent drilling parameters, thereby predicting pullout strength and optimizing holding strength. Therefore, the present work focused on leveraging ML models during RUABD to predict pullout strength at the bone-screw interface. The monitoring of various drilling parameters (including insertion angle, feedrate, rotational speed, and ultrasonic amplitude) was conducted. Multiple ML models were employed to forecast the pullout strength at the interface between bone and screw. The SVR-based ML model exhibited the most accurate prediction among all models, with the lowest error metrics observed. ML algorithms can be leveraged for robust prediction of biomechanical pullout strength to upsurge holding strength and avoid screw loosening.
GokKErdemMKisiogluY, et al. Development of bone chip-vacuum system in orthopedic drilling process. J Braz Soc Mech Sci Eng2021; 43(4): 1–11.
2.
HujaSSLitskyASBeckFM, et al. Pull-out strength of monocortical screws placed in the maxillae and mandibles of dogs. Am J Orthod Dentofacial Orthop2005; 127(3): 307–313.
3.
QasemiMTahmasbiVSheikhiMM, et al. An effect of osteon orientation in end milling operation of cortical bone based on FEM and experiment. J Manuf Process2022; 81: 141–154.
4.
LiuZChenHSuiJ, et al. Failure behavior and influence of surgical tool edges in soft tissue cutting. J Manuf Process2021; 68: 69–78.
5.
JamilMRafiqueSKhanAM, et al. Comprehensive analysis on orthopedic drilling: a state-of-the-art review. Proc IMechE, Part H: J Engineering in Medicine2020; 234(6): 537–561.
6.
YangMLiCSaidZ, et al. Semiempirical heat flux model of hard-brittle bone material in ductile microgrinding. J Manuf Process2021; 71: 501–514.
7.
LiSShuLKizakiT, et al. Cortical bone drilling: a time series experimental analysis of thermal characteristics. J Manuf Process2021; 64: 606–619.
8.
ChenJAnQZouF, et al. Analysis of low-frequency vibration-assisted bone drilling in reducing thermal injury. Mater Manuf Process2021; 36(1): 27–38.
9.
SinghRPPandeyPMMridhaAR, et al. Experimental investigations and statistical modeling of cutting force and torque in rotary ultrasonic bone drilling of human cadaver bone. Proc IMechE, Part H: J Engineering in Medicine2020; 234(2): 148–162.
10.
SinghGPandeyPM.Neck growth kinetics during ultrasonic-assisted sintering of copper powder. Proc IMechE, Part C: J Mechanical Engineering Science2020; 234(11): 2178–2188.
11.
SinghGPandeyPM.Ultrasonic assisted pressureless sintering for rapid manufacturing of complex copper components. Mater Lett2019; 236: 276–280.
12.
SinghGPandeyPM.Rapid manufacturing of copper components using 3D printing and ultrasonic assisted pressureless sintering: Experimental investigations and process optimization. J Manuf Process2019; 43: 253–269.
13.
SinghRPGuptaVPandeyPM, et al. Effect of drilling techniques on microcracks and Pull-Out strength of cortical screw fixed in human tibia: an in-vitro study. Ann Biomed Eng2021; 49(1): 382–393.
14.
AgarwalRJainVGuptaV, et al. Effect of surface topography on pull-out strength of cortical screw after ultrasonic bone drilling: an in vitro study. J Braz Soc Mech Sci Eng2020; 42(7): 1–13.
15.
SorianoJGarayAAristimuñoP, et al. Effects of rotational speed, feed rate and tool type on temperatures and cutting forces when drilling bovine cortical bone. Mach Sci Technol2013; 17(4): 611–636.
16.
GuptaVPandeyPMMridhaAR, et al. Effect of various parameters on the temperature distribution in conventional and diamond coated hollow tool bone drilling: a comparative study. Procedia Eng2017; 184: 90–98.
17.
UdiljakTCiglarDSkoricS.Investigation into bone drilling and thermal bone necrosis. Adv Prod Eng Manag2007; 2(10): 103–112.
18.
FernandesMGAFonsecaEMMNatalRJ.Thermal analysis during bone drilling using rigid polyurethane foams: numerical and experimental methodologies. J Braz Soc Mech Sci Eng2016; 38(7): 1855–1863.
19.
FernandesMGFonsecaEMJorgeRN.Thermo-mechanical stresses distribution on bone drilling: numerical and experimental procedures. Proc IMechE, Part L: J Materials Design and Applications2019; 233(4): 637–646.
20.
LughmaniWABouazza-MaroufKAshcroftI.Drilling in cortical bone: a finite element model and experimental investigations. J Mech Behav Biomed Mater2015; 42: 32–42.
21.
VilimekMHorakZGoldmannT, et al. Experimental investigation of the temperature during bone drilling using thermocouples and numerical finite element analysis. Biotechnol Biotechnol Equip2021; 35(1): 1263–1273.
22.
AmewouiFLe CozGBonnetAS, et al. An analytical modeling with experimental validation of bone temperature rise in drilling process. Med Eng Phys2020; 84: 151–160.
23.
FeldmannAGanserPNolteL, et al. Orthogonal cutting of cortical bone: temperature elevation and fracture toughness. Int J Mach Tools Manuf2017; 118-119: 1–11.
24.
SinghGJainVGuptaD, et al. Parametric effect of vibrational drilling on osteonecrosis and comparative histopathology study with conventional drilling of cortical bone. Proc IMechE, Part H: J Engineering in Medicine2018; 232(10): 975–986.
25.
VafadarSRouhiG.The effects of geometrical parameters of the pedicle screw on its pullout strength: in-vitro animal tests. J Orthop Spine Trauma20173:74189.
26.
OnanA.Ensemble of classifiers and term weighting schemes for sentiment analysis in Turkish. Sci Res Commun2021; 1(1): 1–12.
27.
OnanA.A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Syst Appl2015; 42(20): 6844–6852.
28.
OnanAKorukoğluSBulutH.A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Inf Process Manag2017; 53(4): 814–833.
29.
OnanAKorukoğluSBulutH.Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst Appl2016; 57: 232–247.
30.
OnanATocogluMA.A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access2021; 9: 7701–7722.
31.
OnanA.Hybrid supervised clustering based ensemble scheme for text classification. Kybernetes2017; 46(2): 330–348.
32.
OnanAKorukoğluSBulutH.A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Syst Appl2016; 62: 1–16.
33.
OnanA.Topic-enriched word embeddings for sarcasm identification. In: SilhavyR (ed.) Computer Science on-line Conference, Springer: Springer Cham, 2019, pp.293–304.
34.
PandeyRKPandaSS.A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology. Proc IMechE, Part H: J Engineering in Medicine2014; 228(11): 1135–1145.
35.
PandeyRKPandaSS.Modeling of temperature in orthopaedic drilling using fuzzy logic. Appl Mech Mater2012; 249–250: 1313–1318.
36.
da SilvaFBCorsoLLCostaCA.Optimization of pedicle screw position using finite element method and neural networks. J Braz Soc Mech Sci Eng2021; 43(3): 1–7.
37.
HambaliMSaheedYOladeleT, et al. Adaboost ensemble algorithms for breast cancer classification. J Adv Comput Res Q2019; 10: 1–10.
38.
JoodakiHGepnerBKerriganJ.Leveraging machine learning for predicting human body model response in restraint design simulations. Comput Methods Biomech Biomed Eng2021; 24(6): 597–611.
39.
AgarwalRSinghJGuptaV.An intelligent approach to predict thermal injuries during orthopaedic bone drilling using machine learning. J Braz Soc Mech Sci Eng2022; 44(8): 1–14.
40.
AgarwalRGuptaVSinghJ, et al. Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms. Proc IMechE, Part C: J Mechanical Engineering Science2022; 236(23): 11123–11135.
41.
AgarwalRSinghJGuptaV.Prediction of temperature elevation in rotary ultrasonic bone drilling using machine learning models: an in-vitro experimental study. Med Eng Phys2022; 110: 1–14.
42.
AgarwalRGuptaVJainV.A novel technique of harvesting cortical bone grafts during orthopaedic surgeries. J Braz Soc Mech Sci Eng2021; 43(7): 1–14.
43.
AgarwalRSinghJGuptaV, et al. The concept of rotary ultrasonic bone machining during orthopaedic surgeries. In: SinghYNishantKSinghMR (eds) Advanced manufacturing processes. Boca Raton: CRC Press, 2022, pp.157–172.
44.
AgarwalRGuptaVSinghJ.A novel drill bit design for reducing bone-chip morphology in orthopaedic bone drilling. Mater Today Proc2022; 63: 131–135.
45.
AlamKSilberschmidtVV.Analysis of temperature in conventional and ultrasonically-assisted drilling of cortical bone with infrared thermography. Technol Health Care2014; 22(2): 243–252.
46.
AlamKMitrofanovAVSilberschmidtVV.Experimental investigations of forces and torque in conventional and ultrasonically-assisted drilling of cortical bone. Med Eng Phys2011; 33(2): 234–239.
47.
BoiadjievTBoiadjievGDelchevK, et al. Feed rate control in robotic bone drilling process. Proc IMechE, Part H: J Engineering in Medicine2021; 235(3): 273–280.
48.
OnanA.An ensemble scheme based on language function analysis and feature engineering for text genre classification. J Inf Sci2018; 44(1): 28–47.
49.
IshaqMKwonSKwonS.Short-term energy forecasting framework using an ensemble deep learning approach. IEEE Access2021; 9: 94262–94271.
