Predicting Maximum Process Temperature in Cortical Bone Milling: An XGBoost Approach with Sensitivity Insights

[1] Dahotre, N. B., & Joshi, S. (2016). Machining of bone and hard tissues. Springer. https://doi.org/10.1007/978-3-319-39158-8

[2] Schröter, L., Kaiser, F., Küppers, O., Stein, S., Krüger, B., Wohlfahrt, P., Geroneit, I., Stahlhut, P., Gbureck, U., & Ignatius, A. (2024). Improving bone defect healing using magnesium phosphate granules with tailored degradation characteristics. Dental Materials, 40(3), 508–519. https://doi.org/10.1016/j.dental.2023.12.019

[3] Guenther, D., Schmidl, S., Klatte, T. O., Widhalm, H. K., Omar, M., Krettek, C., Gehrke, T., Kendoff, D., & Haasper, C. (2015). Overweight and obesity in hip and knee arthroplasty: Evaluation of 6,078 cases. World Journal of Orthopedics, 6(1), 137–144. https://doi.org/10.5312/wjo.v6.i1.137

[4] Aliyu, A. A. A., Abdul Rani, A. M., Ginta, T. L., Prakash, C., Axinte, E., Razak, M. A., & Ali, S. (2017). A review of additive mixed electric discharge machining: Current status and future perspectives for surface modification of biomedical implants. Advances in Materials Science and Engineering, 8723239. https://doi.org/10.1155/2017/8723239

[5] Pezzotti, G., & Yamamoto, K. (2014). Artificial hip joints: The biomaterials challenge. Journal of the Mechanical Behavior of Biomedical Materials, 31, 3–20. https://doi.org/10.1016/j.jmbbm.2013.06.001

[6] Plaskos, C. (2002). Bone sawing and milling in computer-assisted total knee arthroplasty [Master’s thesis, University of British Columbia]. https://doi.org/10.14288/1.0080992

[7] Rosen, J., Hannaford, B., & Satava, R. M. (2011). Surgical robotics: Systems applications and visions. Springer Science & Business Media.

[8] Hernandez Montero, E., Caballero, E., & García Ibanez, L. (2020). Surgical management of middle cranial fossa bone defects: Meningoencephalic herniation and cerebrospinal fluid leaks. American Journal of Otolaryngology, 41, 102560. https://doi.org/10.1016/j.amjoto.2020.102560

[9] Dahibhate, R. V., & Jaju, S. B. (2019). Bone drilling parameters and necrosis: An in vitro study. In Smart Technologies for Energy, Environment and Sustainable Development: Select Proceedings of ICSTEESD 2018 (pp. 599–606). Singapore: Springer Singapore.

[10] Gallo, J., Goodman, S. B., Konttinen, Y. T., Wimmer, M. A., & Holinka, M. (2013). Osteolysis around total knee arthroplasty: A review of pathogenetic mechanisms. Acta Biomaterialia, 9, 8046–8058. https://doi.org/10.1016/j.actbio.2013.06.032

[11] Zhang, Y., Robles Linares, J. A., Chen, L., Liao, Z., Shih, A. J., & Wang, C. (2022). Advances in machining of hard tissues – from material removal mechanisms to tooling solutions. International Journal of Machine Tools and Manufacture, 172, 103838. https://doi.org/10.1016/j.ijmachtools.2021.103838

[12] Eriksson, A., & Albrektsson, T. (1983). Temperature threshold levels for heat induced bone tissue injury: A vital microscopic study in the rabbit. The Journal of Prosthetic Dentistry, 50(1), 101–107. https://doi.org/10.1016/0022-3913(83)90174-9

[13] Berman, A. T., Spence, R. J., Yanicko, D. R., Sih, G. C., & Zimmerman, M. (1984). Thermally induced bone necrosis in rabbits: Relation to implant failure in humans. Clinical Orthopaedics and Related Research, 186, 284–292. https://doi.org/10.3109/02844318409052849

[14] Lundskog, J. (1972). An experimental investigation of the thermal properties of bone tissue and threshold levels for thermal injury. Scandinavian Journal of Plastic and Reconstructive Surgery, 9, 1–80.

[15] Bonfield, W., & Li, C. (1968). The temperature dependence of the deformation of bone. Journal of Biomechanics, 1, 323–329. https://doi.org/10.1016/0021-9290(68)90026-2

[16] Eriksson, R., Albrektsson, T., & Magnusson, B. (1984). Assessment of bone viability after heat trauma: A histological, histochemical and vital microscopic study in the rabbit. Scandinavian Journal of Plastic and Reconstructive Surgery, 18(3), 261–268. https://doi.org/10.3109/02844318409052849

[17] Eriksson, R., & Albrektsson, T. (1984). 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, 705–711. https://doi.org/10.1016/0278-2391(84)90417-8

[18] Eriksson, R. A., & Adell, R. (1986). Temperatures during drilling for the placement of implants using the osseointegration technique. Journal of Oral and Maxillofacial Surgery, 44, 4–7. https://doi.org/10.1001/joms.1986.00860030056014

[19] Al Abdullah, K. I. A. L., Abdi, H., Lim, C. P., & Yassin, W. A. (2018). Force and temperature modelling of bone milling using artificial neural networks. Measurement, 116, 25–37. https://doi.org/10.1016/j.measurement.2017.10.051

[20] Liao, Z., Axinte, D., & Gao, D. (2019). On modelling of cutting force and temperature in bone milling. Journal of Materials Processing Technology, 266, 627–638. https://doi.org/10.1016/j.jmatprotec.2018.11.039

[21] Tahmasbi, V., Ghoreishi, M., & Zolfaghari, M. (2017). Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 231(11), 1012–1024. https://doi.org/10.1177/0954411917726098

[22] Hassanalideh, H. H., & Gholampour, S. (2020). Finding the optimal drill bit material and proper drilling condition for utilization in the programming of robot-assisted drilling of bone. CIRP Journal of Manufacturing Science and Technology, 31, 34–47. https://doi.org/10.1016/j.cirpj.2020.09.011

[23] Shin, H., & Yoon, Y.-S. (2006). Bone temperature estimation during orthopaedic round bur milling operations. Journal of Biomechanics, 39, 33–39.

[24] Sugita, N., Osa, T., & Mitsuishi, M. (2009). Analysis and estimation of cutting-temperature distribution during end milling in relation to orthopedic surgery. Medical Engineering & Physics, 31, 101–107. https://doi.org/10.1016/j.medengphy.2008.05.005

[25] Malvisi, A., Vendruscolo, P., Morici, F., Martelli, S., & Marcacci, M. (2000). Milling versus sawing: Comparison of temperature elevation and clinical performance during bone cutting. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 1238–1244). Berlin, Heidelberg: Springer Berlin Heidelberg.

[26] Talebi Ghadikolaee, H., Moslemi Naeini, H., Rabiee, A. H., Zeinolabedin Beygi, A., & Alexandrov, S. (2023). Experimental numerical analysis of ductile damage modeling of aluminum alloy using a hybrid approach: Ductile fracture criteria and adaptive neural fuzzy system (ANFIS). International Journal of Modelling and Simulation, 43(4), 736–751. https://doi.org/10.1080/02286203.2023.11866137

[27] Rabiee, A. H., Sherkatghanad, E., Zeinolabedin Beygi, A., Moslemi Naeini, H., & Lang, L. (2023). Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro fuzzy network. Journal of Computational & Applied Research in Mechanical Engineering (JCARME), 12, 193–209. https://doi.org/10.22059/jcarme.2023.342589.623

[28] Safari, M., Rabiee, A., & Tahmasbi, V. (2022). Resistance spot welding process of AISI 304 steel: Application of sensitivity analysis and ANFIS GWO methods. Journal of Stress Analysis, 6, 21–29. https://doi.org/10.22059/jsa.2022.337368.1060

[29] Liu, C., He, Y., Li, Y., Wang, Y., Wang, L., Wang, S., & Wang, Y. (2021). Predicting residual properties of ball screw raceway in whirling milling based on machine learning. Measurement, 173, 108605. https://doi.org/10.1016/j.measurement.2020.108605

[30] Du, C., Ho, C. L., & Kaminski, J. (2021). Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process. Advances in Manufacturing, 9, 206–215. https://doi.org/10.1007/s40436-021-00385-y

[31] He, Z., Shi, T., Xuan, J., & Li, T. (2021). Research on tool wear prediction based on temperature signals and deep learning. Wear, 478–479, 203902. https://doi.org/10.1016/j.wear.2021.203902

[32] Varghese, A., Kulkarni, V., & Joshi, S. S. (2021). Tool life stage prediction in micro milling from force signal analysis using machine learning methods. Journal of Manufacturing Science and Engineering, 143(5), 054501. https://doi.org/10.1115/1.4048636

[33] Tahmasbi, V., & Rabiee, A. H. (2022). Intelligent temperature modeling in robotic cortical bone milling process based on teaching learning-based optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 236(8), 1118–1128. https://doi.org/10.1177/09544119221106822

[34] Safari, M., Tahmasbi, V., & Rabiee, A. H. (2021). Investigation into the automatic drilling of cortical bones using ANFIS-PSO and sensitivity analysis. Neural Computing and Applications, 33(23), 16499–16517. https://doi.org/10.1007/s00521-021-06248-4

[35] Banda, T., Liu, Y.-C., Farid, A. A., & Lim, C. S. (2023). A machine learning model for flank wear prediction in face milling of Inconel 718. The International Journal of Advanced Manufacturing Technology, 126(7–8), 935–945. https://doi.org/10.1007/s00170-023-11152-3

[36] Rabiee, A. H., Tahmasbi, V., & Qasemi, M. (2023). Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro milling using support vector regression and EFAST methods. Engineering Applications of Artificial Intelligence, 120, 105874. https://doi.org/10.1016/j.engappai.2023.105874

[37] Zeng, S., & Pi, D. (2023). Milling surface roughness prediction based on physics informed machine learning. Sensors, 23, 4969. https://doi.org/10.3390/s23094969

[38] Araghizad, A. E., Pashmforoush, F., Tehranizadeh, F., Kilic, K., & Budak, E. (2024). Improving milling force predictions: A hybrid approach integrating physics based simulation and machine learning for remarkable accuracy across diverse unseen materials and tool types. Journal of Manufacturing Processes, 114, 92–107. https://doi.org/10.1016/j.jmapro.2024.02.015

[39] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785

[40] Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360–3379. https://doi.org/10.1080/10494820.2021.1928235

[41] Wang, C.-C., Kuo, P.-H., & Chen, G.-Y. (2022). Machine learning prediction of turning precision using optimized XGBoost model. Applied Sciences, 12(15), 7739. https://doi.org/10.3390/app12157739

[42] Zhang, X. Y., Trame, M. N., Lesko, L. J., & Schmidt, S. (2015). Sobol sensitivity analysis: A tool to guide the development and evaluation of systems pharmacology models. CPT: Pharmacometrics & Systems Pharmacology, 4(2), 69–79. https://doi.org/10.1002/psp4.6

[43] Nossent, J., Elsen, P., & Bauwens, W. (2011). Sobol’s sensitivity analysis of a complex environmental model. Environmental Modelling & Software, 26, 1515–1525. https://doi.org/10.1016/j.envsoft.2011.06.019

[44] Kumar, D., Singh, A., Kumar, P., Jha, R. K., Sahoo, S. K., & Jha, V. (2020). Sobol sensitivity analysis for risk assessment of uranium in groundwater. Environmental Geochemistry and Health, 42, 1789–1801. https://doi.org/10.1007/s10653-020-00522-5