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Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills

Yıl 2022, Cilt: 11 Sayı: 1, 136 - 141, 25.03.2022
https://doi.org/10.46810/tdfd.1057096

Öz

Orthopedic drills are currently used for various operations in surgical fields such as orthopedics, ear, nose, and throat surgery. The path that orthopedic drills travel through the tissue is controlled manually by surgeons, and manual control leads to the risk of damaging areas such as nerves and tissues. In our study, an innovative approach is presented against existing drill designs and breakthrough detection problems. In the proposed model, the change in the load torque and the change in friction force caused by the tissue change in the drilling path are considered as a disturbance effect, and a disturbance observer has been developed that allows these disturbances to be observed. Observation of the disturbance effects allows the perception of the hardness of tissue change during drilling since it gives the change of load torque changes and friction coefficient, which cannot be measured under normal operation. The performance of the proposed approach has been proven by simulation study.

Destekleyen Kurum

Sivas Cumhuriyet University Scientific Research Grant Program (CUBAP)

Proje Numarası

M737

Teşekkür

The author would like to thank Dr. Ozhan PAZARCI and Ahmet OZTURK for their valuable comments based on their drilling experience.

Kaynakça

  • N. Bertollo and W. Robert, “Drilling of Bone: Practicality, Limitations and Complications Associated with Surgical Drill-Bits,” in Biomechanics in Applications, 2012.
  • E. Gönen, “Minimally invasive surgical techniques for the treatment of the shaft fractures of the long bones,” Türk Ortop. ve Travmatoloji Birliği Derneği Derg., vol. 11, no. 1, pp. 78–88, 2012, doi: 10.5606/totbid.dergisi.2012.11.
  • O. Farouk, C. Krettek, T. Miclau, P. Schandelmaier, P. Guy, and H. Tscherne, “Minimally invasive plate osteosynthesis: Does percutaneous plating disrupt femoral blood supply less than the traditional technique?,” J. Orthop. Trauma, 1999, doi: 10.1097/00005131-199908000-00002.
  • Y. Torun and A. Öztürk, “A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach,” Ann. Biomed. Eng., vol. 48, no. 4, 2020, doi: 10.1007/s10439-019-02444-5.
  • R. A. Modi and R. P. Nayak, “Detection of Breakthrough During Bone-Drilling in Orthopaedic Surgery,” vol. 1, no. 9, pp. 794–798, 2014.
  • Y. Torun, A. Ozturk, N. Hatipoglu, and Z. Oztemur, “Detection of Bone Excretion with Current Sensor in Robotic Surgery,” in UBMK 2018 - 3rd International Conference on Computer Science and Engineering, 2018, doi: 10.1109/UBMK.2018.8566443.
  • A. Öztürk, “Robotik cerrahi matkaplarda güç analizi ile matkap ucu çıkış tespiti,” Cumhuriyet Üniversitesi Fen Bilimleri Enstitüsü, 2019.
  • Y. Torun, O. Pazarci, and A. Ozturk, “Current Approaches to Bone-Drilling Procedures with Orthopedic Drills,” Cyprus J. Med. Sci., vol. 5, no. 1, pp. 93–98, 2020, doi: 10.5152/cjms.2020.1242.
  • G. Augustin et al., “Cortical bone drilling and thermal osteonecrosis,” Clinical Biomechanics, vol. 27, no. 4. 2012, doi: 10.1016/j.clinbiomech.2011.10.010.
  • M. Praamsma, H. Carnahan, D. Backstein, C. J. H. Veillette, D. Gonzalez, and A. Dubrowski, “Drilling sounds are used by surgeons and intermediate residents, but not novice orthopedic trainees, to guide drilling motions,” Can. J. Surg., 2008.
  • D. Ho, T. Li, and Q. H. Meng, “Bone Drilling Breakthrough Detection via Energy-Based Signal,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, vol. 2018-July, doi: 10.1109/EMBC.2018.8512621.
  • G. Zheng and L. P. Nolte, “Computer-Assisted Orthopedic Surgery: Current State and Future Perspective,” Frontiers in Surgery, vol. 2. 2015, doi: 10.3389/fsurg.2015.00066
  • Y. Torun and Ö. Pazarci, “Parametric Power Spectral Density Estimation-Based Breakthrough Detection for Orthopedic Bone Drilling with Acoustic Emission Signal Analysis,” Acoust. Aust., vol. 48, no. 2, 2020, doi: 10.1007/s40857-020-00182-6.
  • Y. Torun, A. Ozturk, N. Hatipoglu, and Z. Oztemur, “Breakthrough detection for orthopedic bone drilling via power spectral density estimation of acoustic emission,” 2018 Electr. Electron. Comput. Sci. Biomed. Eng. Meet. EBBT 2018, pp. 1–5, 2018, doi: 10.1109/EBBT.2018.8391464.
  • Z. Ying, L. Shu, and N. Sugita, “Autonomous Penetration Perception for Bone Cutting during Laminectomy,” in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Nov. 2020, pp. 1043–1048, doi: 10.1109/BioRob49111.2020.9224375
  • M. Seibold et al., “Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery,” Sci. Rep., vol. 11, no. 1, p. 3993, 2021, doi: 10.1038/s41598-021-83506-4.
  • T. Osa et al., “Hand-Held Bone Cutting Tool with Autonomous Penetration Detection for Spinal Surgery,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 6, pp. 3018–3027, 2015, doi: 10.1109/TMECH.2015.2410287.
  • A. Hingmire and B. B. Pimple, “Simulation and Analysis Studies of Speed Control of Brushless DC Motor Using Hall Sensors,” in 2018 International Conference on Smart Electric Drives and Power System (ICSEDPS), 2018, pp. 384–387, doi: 10.1109/ICSEDPS.2018.8536062.
  • S. K. Safi, “Analysis and simulation of the high-speed torque performance of brushless DC motor drives,” IEE Proc. - Electr. Power Appl., vol. 142, no. 3, 1995, doi: 10.1049/ip-epa:19951808.
  • Ö. Aydoğdu, “Fırçasız doğru akım motorlarının genetik tabanlı bulanık denetleyici ile sensörsüz kontrolü,” Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2006.
  • X. Li, “Model-Based Design of Brushless Dc Motor Control and Motion Control Modelling for Robocup Ssl,” 2015.
  • W. H. Chen, D. J. Ballance, P. J. Gawthrop, and J. O’Reilly, “A nonlinear disturbance observer for robotic manipulators,” IEEE Trans. Ind. Electron., vol. 47, no. 4, pp. 932–938, 2000, doi: 10.1109/41.857974.
  • A. Mohammadi, M. Tavakoli, H. J. Marquez, and F. Hashemzadeh, “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract., vol. 21, no. 3, 2013, doi: 10.1016/j.conengprac.2012.10.008.
  • X. Wen, “Enhanced disturbance-observer-based control for a class of time-delay system with uncertain sinusoidal disturbances,” Math. Probl. Eng., vol. 2013, 2013, doi: 10.1155/2013/805687.
  • A. Radke and Z. Gao, “A survey of state and disturbance observers for practitioners,” in Proceedings of the American Control Conference, 2006, vol. 2006, doi: 10.1109/acc.2006.1657545.
  • S. C. Lee and H. S. Ahn, “Sensorless torque estimation using adaptive Kalman filter and disturbance estimator,” in Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2010, 2010, doi: 10.1109/MESA.2010.5552094.
  • O. Kouhei, M. Shibata, and T. Murakami, “Motion control for advanced mechatronics,” IEEE/ASME Trans. Mechatronics, vol. 1, no. 1, 1996, doi: 10.1109/3516.491410.
  • A. R. Eriksson, T. Albrektsson, and B. Albrektsson, “Heat caused by drilling cortical bone: Temperature measured in vivo in patients and animals,” Acta Orthop., vol. 55, no. 6, 1984, doi: 10.3109/17453678408992410.
  • K. N. Bachus, M. T. Rondina, and D. T. Hutchinson, “The effects of drilling force on cortical temperatures and their duration: An in vitro study,” Med. Eng. Phys., vol. 22, no. 10, 2000, doi: 10.1016/S1350-4533(01)00016-9.
  • Y. TORUN and S. MALATYALI, “POWER ANALYSIS OF ROBOTIC MEDICAL DRILL WITH DIFFERENT CONTROL APPROACHES,” Cumhur. Sci. J., vol. 41, no. 2, 2020, doi: 10.17776/csj.661666.
  • F. Amewoui, G. Le Coz, A. S. Bonnet, and A. Moufki, “Bone drilling: an identification of heat sources,” Comput. Methods Biomech. Biomed. Engin., vol. 23, no. sup1, 2020, doi: 10.1080/10255842.2020.1813418.
  • K. Alam, S. Piya, A. Al-Ghaithi, and V. Silberschmidth, “Experimental investigation on the effect of drill quality on the performance of bone drilling,” Biomed. Tech., vol. 65, no. 1, 2020, doi: 10.1515/bmt-2018-0184.
  • Y. Torun and S. Malatyalı, “Power Analysis of Robotic Medical Drill with Different Control Approaches,” Cumhur. Sci. J., vol. 41, no. 2, pp. 527–533, 2020, doi: 10.17776/csj.661666.
  • G. Boiadjiev, I. Chavdarov, K. Delchev, T. Boiadjiev, R. Kastelov, and K. Zagurki, “Development of Hand-Held Surgical Robot ODRO-2 for Automatic Bone Drilling,” J. Theor. Appl. Mech., vol. 47, no. 4, pp. 12–22, 2017, doi: 10.1515/jtam-2017-0017.

Ortopedik Matkaplar İçin Bozucu Gözlemci Tabanlı Kemik Doku Değişim Tahmin Yaklaşımı Benzetimi

Yıl 2022, Cilt: 11 Sayı: 1, 136 - 141, 25.03.2022
https://doi.org/10.46810/tdfd.1057096

Öz

Günümüzde ortopedi, kulak burun boğaz gibi cerrahi alanlarda çeşitli operasyonlarda ortopedik matkaplar kullanılmaktadır. Ortopedik matkapların doku içerisindeki kat ettiği yol manuel olarak cerrahlar tarafından kontrol edilmektedir ve manuel kontrol sinir, doku gibi bölgelerde hasar oluşturma riskine yol açmaktadır. Çalışmamızda mevcut matkap tasarımlarına ve sorunlarına karşı yenilikçi bir model sunulmaktadır. Önerilen modelde yük torqueindeki değişim ve matkap ucundaki doku değişikliğinden kaynaklanan sürtünme kuvveti değişimi bozucu etki olarak ele alınmış, bu bozucu etkilerin gözlemlenmesine olanak sağlayan bir bozucu gözlemci geliştirilmiştir. Bozucu etkilerinin gözlemlenmesi, normal şartlarda ölçülemeyen yük torque değişimlerinin ve sürtünme katsayısının değişimini verdiğinden dolayı, delme esnasında doku değişiminin algılanmasına olanak sağlamaktadır. Önerilen yöntemi başarımı benzetim çalışmaları ile kanıtlanmıştır.

Proje Numarası

M737

Kaynakça

  • N. Bertollo and W. Robert, “Drilling of Bone: Practicality, Limitations and Complications Associated with Surgical Drill-Bits,” in Biomechanics in Applications, 2012.
  • E. Gönen, “Minimally invasive surgical techniques for the treatment of the shaft fractures of the long bones,” Türk Ortop. ve Travmatoloji Birliği Derneği Derg., vol. 11, no. 1, pp. 78–88, 2012, doi: 10.5606/totbid.dergisi.2012.11.
  • O. Farouk, C. Krettek, T. Miclau, P. Schandelmaier, P. Guy, and H. Tscherne, “Minimally invasive plate osteosynthesis: Does percutaneous plating disrupt femoral blood supply less than the traditional technique?,” J. Orthop. Trauma, 1999, doi: 10.1097/00005131-199908000-00002.
  • Y. Torun and A. Öztürk, “A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach,” Ann. Biomed. Eng., vol. 48, no. 4, 2020, doi: 10.1007/s10439-019-02444-5.
  • R. A. Modi and R. P. Nayak, “Detection of Breakthrough During Bone-Drilling in Orthopaedic Surgery,” vol. 1, no. 9, pp. 794–798, 2014.
  • Y. Torun, A. Ozturk, N. Hatipoglu, and Z. Oztemur, “Detection of Bone Excretion with Current Sensor in Robotic Surgery,” in UBMK 2018 - 3rd International Conference on Computer Science and Engineering, 2018, doi: 10.1109/UBMK.2018.8566443.
  • A. Öztürk, “Robotik cerrahi matkaplarda güç analizi ile matkap ucu çıkış tespiti,” Cumhuriyet Üniversitesi Fen Bilimleri Enstitüsü, 2019.
  • Y. Torun, O. Pazarci, and A. Ozturk, “Current Approaches to Bone-Drilling Procedures with Orthopedic Drills,” Cyprus J. Med. Sci., vol. 5, no. 1, pp. 93–98, 2020, doi: 10.5152/cjms.2020.1242.
  • G. Augustin et al., “Cortical bone drilling and thermal osteonecrosis,” Clinical Biomechanics, vol. 27, no. 4. 2012, doi: 10.1016/j.clinbiomech.2011.10.010.
  • M. Praamsma, H. Carnahan, D. Backstein, C. J. H. Veillette, D. Gonzalez, and A. Dubrowski, “Drilling sounds are used by surgeons and intermediate residents, but not novice orthopedic trainees, to guide drilling motions,” Can. J. Surg., 2008.
  • D. Ho, T. Li, and Q. H. Meng, “Bone Drilling Breakthrough Detection via Energy-Based Signal,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, vol. 2018-July, doi: 10.1109/EMBC.2018.8512621.
  • G. Zheng and L. P. Nolte, “Computer-Assisted Orthopedic Surgery: Current State and Future Perspective,” Frontiers in Surgery, vol. 2. 2015, doi: 10.3389/fsurg.2015.00066
  • Y. Torun and Ö. Pazarci, “Parametric Power Spectral Density Estimation-Based Breakthrough Detection for Orthopedic Bone Drilling with Acoustic Emission Signal Analysis,” Acoust. Aust., vol. 48, no. 2, 2020, doi: 10.1007/s40857-020-00182-6.
  • Y. Torun, A. Ozturk, N. Hatipoglu, and Z. Oztemur, “Breakthrough detection for orthopedic bone drilling via power spectral density estimation of acoustic emission,” 2018 Electr. Electron. Comput. Sci. Biomed. Eng. Meet. EBBT 2018, pp. 1–5, 2018, doi: 10.1109/EBBT.2018.8391464.
  • Z. Ying, L. Shu, and N. Sugita, “Autonomous Penetration Perception for Bone Cutting during Laminectomy,” in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Nov. 2020, pp. 1043–1048, doi: 10.1109/BioRob49111.2020.9224375
  • M. Seibold et al., “Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery,” Sci. Rep., vol. 11, no. 1, p. 3993, 2021, doi: 10.1038/s41598-021-83506-4.
  • T. Osa et al., “Hand-Held Bone Cutting Tool with Autonomous Penetration Detection for Spinal Surgery,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 6, pp. 3018–3027, 2015, doi: 10.1109/TMECH.2015.2410287.
  • A. Hingmire and B. B. Pimple, “Simulation and Analysis Studies of Speed Control of Brushless DC Motor Using Hall Sensors,” in 2018 International Conference on Smart Electric Drives and Power System (ICSEDPS), 2018, pp. 384–387, doi: 10.1109/ICSEDPS.2018.8536062.
  • S. K. Safi, “Analysis and simulation of the high-speed torque performance of brushless DC motor drives,” IEE Proc. - Electr. Power Appl., vol. 142, no. 3, 1995, doi: 10.1049/ip-epa:19951808.
  • Ö. Aydoğdu, “Fırçasız doğru akım motorlarının genetik tabanlı bulanık denetleyici ile sensörsüz kontrolü,” Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2006.
  • X. Li, “Model-Based Design of Brushless Dc Motor Control and Motion Control Modelling for Robocup Ssl,” 2015.
  • W. H. Chen, D. J. Ballance, P. J. Gawthrop, and J. O’Reilly, “A nonlinear disturbance observer for robotic manipulators,” IEEE Trans. Ind. Electron., vol. 47, no. 4, pp. 932–938, 2000, doi: 10.1109/41.857974.
  • A. Mohammadi, M. Tavakoli, H. J. Marquez, and F. Hashemzadeh, “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract., vol. 21, no. 3, 2013, doi: 10.1016/j.conengprac.2012.10.008.
  • X. Wen, “Enhanced disturbance-observer-based control for a class of time-delay system with uncertain sinusoidal disturbances,” Math. Probl. Eng., vol. 2013, 2013, doi: 10.1155/2013/805687.
  • A. Radke and Z. Gao, “A survey of state and disturbance observers for practitioners,” in Proceedings of the American Control Conference, 2006, vol. 2006, doi: 10.1109/acc.2006.1657545.
  • S. C. Lee and H. S. Ahn, “Sensorless torque estimation using adaptive Kalman filter and disturbance estimator,” in Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2010, 2010, doi: 10.1109/MESA.2010.5552094.
  • O. Kouhei, M. Shibata, and T. Murakami, “Motion control for advanced mechatronics,” IEEE/ASME Trans. Mechatronics, vol. 1, no. 1, 1996, doi: 10.1109/3516.491410.
  • A. R. Eriksson, T. Albrektsson, and B. Albrektsson, “Heat caused by drilling cortical bone: Temperature measured in vivo in patients and animals,” Acta Orthop., vol. 55, no. 6, 1984, doi: 10.3109/17453678408992410.
  • K. N. Bachus, M. T. Rondina, and D. T. Hutchinson, “The effects of drilling force on cortical temperatures and their duration: An in vitro study,” Med. Eng. Phys., vol. 22, no. 10, 2000, doi: 10.1016/S1350-4533(01)00016-9.
  • Y. TORUN and S. MALATYALI, “POWER ANALYSIS OF ROBOTIC MEDICAL DRILL WITH DIFFERENT CONTROL APPROACHES,” Cumhur. Sci. J., vol. 41, no. 2, 2020, doi: 10.17776/csj.661666.
  • F. Amewoui, G. Le Coz, A. S. Bonnet, and A. Moufki, “Bone drilling: an identification of heat sources,” Comput. Methods Biomech. Biomed. Engin., vol. 23, no. sup1, 2020, doi: 10.1080/10255842.2020.1813418.
  • K. Alam, S. Piya, A. Al-Ghaithi, and V. Silberschmidth, “Experimental investigation on the effect of drill quality on the performance of bone drilling,” Biomed. Tech., vol. 65, no. 1, 2020, doi: 10.1515/bmt-2018-0184.
  • Y. Torun and S. Malatyalı, “Power Analysis of Robotic Medical Drill with Different Control Approaches,” Cumhur. Sci. J., vol. 41, no. 2, pp. 527–533, 2020, doi: 10.17776/csj.661666.
  • G. Boiadjiev, I. Chavdarov, K. Delchev, T. Boiadjiev, R. Kastelov, and K. Zagurki, “Development of Hand-Held Surgical Robot ODRO-2 for Automatic Bone Drilling,” J. Theor. Appl. Mech., vol. 47, no. 4, pp. 12–22, 2017, doi: 10.1515/jtam-2017-0017.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Yunis Torun 0000-0002-6187-0451

Proje Numarası M737
Yayımlanma Tarihi 25 Mart 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 11 Sayı: 1

Kaynak Göster

APA Torun, Y. (2022). Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills. Türk Doğa Ve Fen Dergisi, 11(1), 136-141. https://doi.org/10.46810/tdfd.1057096
AMA Torun Y. Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills. TDFD. Mart 2022;11(1):136-141. doi:10.46810/tdfd.1057096
Chicago Torun, Yunis. “Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills”. Türk Doğa Ve Fen Dergisi 11, sy. 1 (Mart 2022): 136-41. https://doi.org/10.46810/tdfd.1057096.
EndNote Torun Y (01 Mart 2022) Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills. Türk Doğa ve Fen Dergisi 11 1 136–141.
IEEE Y. Torun, “Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills”, TDFD, c. 11, sy. 1, ss. 136–141, 2022, doi: 10.46810/tdfd.1057096.
ISNAD Torun, Yunis. “Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills”. Türk Doğa ve Fen Dergisi 11/1 (Mart 2022), 136-141. https://doi.org/10.46810/tdfd.1057096.
JAMA Torun Y. Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills. TDFD. 2022;11:136–141.
MLA Torun, Yunis. “Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills”. Türk Doğa Ve Fen Dergisi, c. 11, sy. 1, 2022, ss. 136-41, doi:10.46810/tdfd.1057096.
Vancouver Torun Y. Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills. TDFD. 2022;11(1):136-41.