NUMERICAL PREDICTION OF THE COMPONENT-RATIO-DEPENDENT COMPRESSIVE STRENGTH OF BONE CEMENT
Anna MACHROWSKA
a.machrowska@pollub.plLublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Robert KARPIŃSKI
Lublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Józef JONAK
Lublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Jakub SZABELSKI
Lublin University of Technology, Faculty of Mechanical Engineering Department of Computerization and Production Robotization, Section of Biomedical Engineering, Nadbystrzycka 36, 20-618 Lublin (Poland)
Abstract
Changes in the compression strength of the PMMA bone cement with a variable powder/liquid component mix ratio were investigated. The strength test data served to develop basic mathematical models and an artificial neural network was employed for strength predictions. The empirical and numerical results were compared to determine modelling errors and assess the effectiveness of the proposed methods and models. The advantages and disadvantages of mathematical modelling are discussed.
Keywords:
artificial neural networks, mathematical modelling, biomaterials, bone cementReferences
Balin, A. (2004). Materiałowo uwarunkowane procesy adaptacyjne i trwałość cementów stosowanych w chirurgii kostnej. Wydawnictwo Politechniki Śląskiej.
Google Scholar
Balin, A. (2016). Cementy w chirurgii kostnej. Wydawnictwo Politechniki Śląskiej. Bialoblocka-Juszczyk, E., Baleani, M., Cristofolini, L., & Viceconti, M. (2008). Fracture Properties of an Acrylic Bone Cement. Acta of Bioengineering and Biomechanics, 10(1), 21–26.
Google Scholar
Charnley, J. (1960). Anchorage of the Femoral Head Prosthesis to the Shaft of the Femur. The Journal of Bone and Joint Surgery. British Volume, 42(1), 28–30. https://doi.org/10.1302/0301-620X.42B1.28
DOI: https://doi.org/10.1302/0301-620X.42B1.28
Google Scholar
Chen, X., Zhang, L., Liu, T., & Kamruzzaman, M.M. (2019). Research on Deep Learning in the Field of Mechanical Equipment Fault Diagnosis Image Quality. Journal of Visual Communication and Image Representation, 62, 402–409. https://doi.org/10.1016/j.jvcir.2019.06.007
DOI: https://doi.org/10.1016/j.jvcir.2019.06.007
Google Scholar
Dunne, N.J., & Orr, J.F. (2001). Influence of Mixing Techniques on the Physical Properties of Acrylic Bone Cement. Biomaterials, 22(13), 1819-1826. https://doi.org/10.1016/S0142-9612(00)00363-X
DOI: https://doi.org/10.1016/S0142-9612(00)00363-X
Google Scholar
Dunne, N.J., Orr, J.F., Mushipe, M.T., & Eveleigh, R.J. (2003). The Relationship between Porosity and Fatigue Characteristics of Bone Cements. Biomaterials, 24(2), 239–245. https://doi.org/10.1016/S0142-9612(02)00296-X
DOI: https://doi.org/10.1016/S0142-9612(02)00296-X
Google Scholar
Falkowicz, K., & Debski, H. (2019). The Work of a Compressed, Composite Plate in Asymmetrical Arrangement of Layers. AIP Conference Proceedings, 2078, 020005. https://doi.org/10.1063/1.5092008
DOI: https://doi.org/10.1063/1.5092008
Google Scholar
Falkowicz, K., & Debski, H. (2020). The Post-Critical Behaviour of Compressed Plate with NonStandard Play Orientation. Composite Structures, 252, 112701. https://doi.org/10.1016/j.compstruct.2020.112701
DOI: https://doi.org/10.1016/j.compstruct.2020.112701
Google Scholar
Falkowicz, K., Debski, H., & Wysmulski, P. (2020). Effect of Extension-Twisting and ExtensionBending Coupling on a Compressed Plate with a Cut-Out. Composite Structures, 238, 111941. https://doi.org/10.1016/j.compstruct.2020.111941
DOI: https://doi.org/10.1016/j.compstruct.2020.111941
Google Scholar
de Haan, K., Rivenson, Y., Wu, Y., & Ozcan, A. (2020). Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy. Proceedings of the IEEE, 108(1), 30–50. https://doi.org/10.1109/JPROC.2019.2949575
DOI: https://doi.org/10.1109/JPROC.2019.2949575
Google Scholar
Hatt, M., Parmar, Ch., Qi, J., & El Naqa, I. (2019). Machine (Deep) Learning Methods for Image Processing and Radiomics. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 104–108. https://doi.org/10.1109/TRPMS.2019.2899538
DOI: https://doi.org/10.1109/TRPMS.2019.2899538
Google Scholar
Hosseini, M.P., Hosseini, A., & Ahi, K. (2020). A Review on Machine Learning for EEG Signal Processing in Bioengineering. In IEEE Reviews in Biomedical Engineering (p. 1–1). IEEE. https://doi.org/10.1109/RBME.2020.2969915
DOI: https://doi.org/10.1109/RBME.2020.2969915
Google Scholar
Jiménez, G., & Racoceanu, D. (2019). Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading. Frontiers in Bioengineering and Biotechnology, 7, 145. https://doi.org/10.3389/fbioe.2019.00145
DOI: https://doi.org/10.3389/fbioe.2019.00145
Google Scholar
Karpiński, R., Szabelski, J., & Maksymiuk, J. (2018). Analysis of the Properties of Bone Cement with Respect to Its Manufacturing and Typical Service Lifetime Conditions. MATEC Web of Conferences, 244, 01004. https://doi.org/10.1051/matecconf/201824401004
DOI: https://doi.org/10.1051/matecconf/201824401004
Google Scholar
Karpiński, R., Szabelski, J., & Maksymiuk, J. (2019a). Effect of Physiological Fluids Contamination on Selected Mechanical Properties of Acrylate Bone Cement. Materials, 12(23), 3963. https://doi.org/10.3390/ma12233963
DOI: https://doi.org/10.3390/ma12233963
Google Scholar
Karpiński, R., Szabelski, J., & Maksymiuk, J. (2019b). Seasoning Polymethyl Methacrylate (PMMA) Bone Cements with Incorrect Mix Ratio. Materials, 12(19), 3073. https://doi.org/10.3390/ma12193073
DOI: https://doi.org/10.3390/ma12193073
Google Scholar
Lee, S.M., Seo, J.B., Yun, J., Cho, Y.-H., Vogel-Claussen, J., Schiebler, M.L., Gefter, W.B., van Beek, E.J.R., Goo, J.M., Lee, K.S., Hatabu, H., Gee, J., & Kim, N. (2019). Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art. Journal of Thoracic Imaging, 34(2), 75–85. https://doi.org/10.1097/RTI.0000000000000387
DOI: https://doi.org/10.1097/RTI.0000000000000387
Google Scholar
Lelovics, H., & Liptáková, T. (2019). Comparison of Some Mechanical and Rheological Properties of Bone Cements’. In 5th Danubia – Adria Symposium on Advances in Experimental Mechanics (pp. 157–158). Czech Republic.
Google Scholar
Lelovics, H., & Liptakova, T. (2010). Time and Mixing Techniquedependent Changes in Bone Cement SmartSet (R) HV. Acta of Bioengineering and Biomechanics, 12(4), 63–67.
DOI: https://doi.org/10.26552/com.C.2010.4.85-89
Google Scholar
Liptáková, T., Lelovics, H., & Necas, L. (2009). Variations of Temperature of Acrylic Bone Cements Prepared by Hand and Vacuum Mixing during Their Polymerization. Acta of Bioengineering and Biomechanics, 11(3), 47–51.
Google Scholar
Matuszewski, Ł., Olchowik, G., Mazurkiewicz, T., Kowalczyk, B., Zdrojewska, A., Matuszewska, A., Ciszewski, A., Gospodarek, M., & Morawik, I. (2014). Biomechanical Parameters of the BP-Enriched Bone Cement. European Journal of Orthopaedic Surgery & Traumatology, 24(4), 435–441. https://doi.org/10.1007/s00590-013-1230-1
DOI: https://doi.org/10.1007/s00590-013-1230-1
Google Scholar
Pałubicka, A., Czubek, J., & Wekwejt, M. (2019). Effect of Aeration of Antibiotic-Loaded Bone Cement on Its Properties and Bactericidal Effectiveness. Minerva Ortopedica e Traumatologica, 70(2), 78–85. https://doi.org/10.23736/S0394-3410.19.03913-4
DOI: https://doi.org/10.23736/S0394-3410.19.03913-4
Google Scholar
Tan, J.H., Koh, B.Th., Ramruttun, A.K., & Wang, W. (2016). Compression and Flexural Strength of Bone Cement Mixed with Blood. Journal of Orthopaedic Surgery (Hong Kong), 24(2), 240–244. https://doi.org/10.1177/1602400223
DOI: https://doi.org/10.1177/1602400223
Google Scholar
Tu, Y.-H., Du, J., & Lee, Ch.-H. (2019). Speech Enhancement Based on Teacher–Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(12), 2080-2091. https://doi.org/10.1109/TASLP.2019.2940662
DOI: https://doi.org/10.1109/TASLP.2019.2940662
Google Scholar
Wekwejt, M., Michalska-Sionkowska, M., Bartmański, M., Nadolska, M., Łukowicz, K., Pałubicka, A., Osyczka, A.M., & Zieliński, A. (2020). Influence of Several Biodegradable Components Added to Pure and Nanosilver-Doped PMMA Bone Cements on Its Biological and Mechanical Properties. Materials Science and Engineering: C, 117, 111286. https://doi.org/10.1016/j.msec.2020.111286
DOI: https://doi.org/10.1016/j.msec.2020.111286
Google Scholar
Wekwejt, M., Moritz, N., Świeczko-Żurek, B., & Pałubicka, A. (2018). Biomechanical Testing of Bioactive Bone Cements – a Comparison of the Impact of Modifiers: Antibiotics and Nanometals. Polymer Testing, 70, 234–243. https://doi.org/10.1016/j.polymertesting.2018.07.014
DOI: https://doi.org/10.1016/j.polymertesting.2018.07.014
Google Scholar
Wekwejt, M., Michno, A., Truchan, K., Pałubicka, A., Świeczko-Żurek, B., Osyczka, A.M., & Zieliński, A. (2019). Antibacterial Activity and Cytocompatibility of Bone Cement Enriched with Antibiotic, Nanosilver, and Nanocopper for Bone Regeneration. Nanomaterials, 9(8), 1114. doi: https://doi.org/10.3390/nano9081114
DOI: https://doi.org/10.3390/nano9081114
Google Scholar
Younesi, M., Bahrololoom, M.E., & Ahmadzadeh, M. (2010). Prediction of Wear Behaviors of Nickel Free Stainless Steel–Hydroxyapatite Bio-Composites Using Artificial Neural Network. Computational Materials Science, 47(3), 645–54.
Google Scholar
https://doi.org/10.1016/j.commatsci.2009.09.019
DOI: https://doi.org/10.1016/j.commatsci.2009.09.019
Google Scholar
Zhang, W., Cui, X., Finkler, U., Kingsbury, B., Saon, G., Kung, D., & Picheny, M. (2019). Distributed Deep Learning Strategies for Automatic Speech Recognition. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5706–5710). Brighton, United Kingdom: IEEE.
DOI: https://doi.org/10.1109/ICASSP.2019.8682888
Google Scholar
Authors
Anna MACHROWSKAa.machrowska@pollub.pl
Lublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Robert KARPIŃSKILublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Józef JONAKLublin University of Technology Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Jakub SZABELSKILublin University of Technology, Faculty of Mechanical Engineering Department of Computerization and Production Robotization, Section of Biomedical Engineering, Nadbystrzycka 36, 20-618 Lublin Poland
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