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Power analysis of robotic medical drill with different control approaches

Yıl 2020, Cilt: 41 Sayı: 2, 527 - 533, 25.06.2020

Yunis Torun , Sefa Malatyalı

https://doi.org/10.17776/csj.661666
Cited By: 2

Öz

Increasing the efficiency of the systems used in surgical operations has become an important issue. Especially in orthopedic surgery, many surgical systems and instruments are used to reduce the workload of surgeons and increase the success of the operation. Surgical drills, which are one of these systems used in orthopedic surgery, are used in operations such as drilling, cutting and carving in various interventions. Cases such as drill sensitivity and stability are critical to operational success and patient health. In this study, an orthopedic drill design that can be added to a linear motion module or a 6-axis robot manipulator has been realized. Linear Quadratic Regulator (LQR), which is one of the optimal controller methods, Proportional Integral (PI) Controller, which is one of the classical controller methods and Model Predictive Controller (MPC) systems from modern controller systems are designed to perform speed control task of the surgical drill. A drill integrated into the robot manipulator for a constant drilling speed of 120 rad/sec and a robot manipulator were used to provide constant feed rate (1 mm/s) and to drill holes at constant intervals during the drilling experiments. Power analysis is performed in real-time in bone drilling operations for three controllers. Current, and voltage information during drilling are recorded simultaneously in the experimental setup. In particular, it has been observed that the power signal and the force information of the bone in different layers are proportional.

Anahtar Kelimeler

Bone Drilling Robotic Surgery Power Analysis

Destekleyen Kurum

CUBAP

Proje Numarası

M-737

Teşekkür

The authors thank Dr. Özhan Pazarcı for his valuable assistance in bone drilling experiments

Kaynakça

  • [1] Dai Y.Xue Y.and Zhang J., Condition monitoring based on sound feature extraction during bone drilling process, Proceedings of the 33rd Chinese Control Conference, (2014) 7317–7322.
  • [2] Chung G.B., Lee S.G., Kim S., Yi B.J., Kim W.K., Oh S.M., Kim Y.S., Park J.and Oh S.H., A robot-assisted surgery system for spinal fusion, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, (2005) 3744–3750.
  • [3] Torun Y., Ozturk A., Hatipoglu N.and Oztemur Z., Detection of Bone Excretion with Current Sensor in Robotic Surgery, UBMK 2018 - 3rd International Conference on Computer Science and Engineering, (2018) 185-189.
  • [4] Duan X., Al-Qwbani M., Zeng Y., Zhang W. and Xiang Z., Intramedullary nailing for tibial shaft fractures in adults, Cochrane Database of Systematic Reviews, (2012) CD008241.
  • [5] Osa T., Abawi C.F., Sugita N., Chikuda H., Sugita S., Tanaka T., Oshima H., Moro T., Tanaka S.and Mitsuishi M., Hand-Held Bone Cutting Tool with Autonomous Penetration Detection for Spinal Surgery, IEEE/ASME Transactions on Mechatronics, 20(6) (2015) 3018–3027.
  • [6] Torun Y., Ozturk A., Aksoz A. and Pazarci O., "Parameters Estimation of Orthopedic Drill," 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, (2019) 1–4.
  • [7] Carroll A. and Heiser G., An Analysis of Power Consumption in a Smartphone., USENIX annual technical conference, (2010) 271–285.
  • [8] Torun, Y., Öztürk, A., A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach., Ann Biomed Eng., 48 (2020) 1218–1229.
  • [9] Lee W.-Y. and Shih C.-L., Control and breakthrough detection of a three-axis robotic bone drilling system, Mechatronics, 2(16) (2006) 73–84.
  • [10] Farnworth G.H. and Burton J.A., Optimization of Drill Geometry for Orthopaedic Surgery, Proceedings of the Fifteenth International Machine Tool Design and Research Conference, (2015) 227–233.
  • [11] Deng Z., Jin H., Hu Y., He Y., Zhang P., Tian W.and Zhang J., Fuzzy force control and state detection in vertebral lamina milling, Mechatronics, 35 (2016) 1–10.
  • [12] Díaz I., Gil J.J.and Louredo M., Bone drilling methodology and tool based on position measurements, Computer Methods and Programs in Biomedicine, 2(112) (2013) 284–292
  • [13] Mayer M., Lin H.H., Peng Y.H., Lee P.Y.and Wang M.L., A drill signal detection technology for handheld medical drilling device, Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014, (2014) 958–961.
  • [14] Turner C.H. and Burr D.B., Basic biomechanical measurements of bone: A tutorial, Bone, 4(14) (1993) 595–608.
  • [15] Zioupos P., Currey J.D., Hamer A.J., The role of collagen in the declining mechanical properties of aging human cortical bone. J Biomed Mater Res. 45(2) (1999) 108-116.
  • [16] Radcliffe P. and Kumar D., Sensorless speed measurement for brushed DC motors, IET Power Electronics, 8(11) (2015) 2223–2228.
  • [17] Farrell M., Jackson J., Nielsen J., Bidstrup C. and McLain T., Error-State LQR Control of a Multirotor UAV, 2019 International Conference on Unmanned Aircraft Systems (ICUAS), (2019) 704–711.
  • [18] Nafea M., Ali A.R.M., Baliah J.and Ali M.S.M., Metamodel-based optimization of a PID controller parameters for a coupled-tank system, Telkomnika (Telecommunication Computing Electronics and Control), 16(4) (2018) 1590–1596.
  • [19] Samin R.E., Jie L.M.and Zawawi M.A., PID implementation of heating tank in mini automation plant using programmable logic controller (PLC), InECCE 2011 - International Conference on Electrical, Control and Computer Engineering, (2011) 515–519.
  • [20] Bai T.Li S. and Zheng Y., Distributed model predictive control for networked plant-wide systems with neighborhood cooperation, IEEE/CAA Journal of Automatica Sinica, 6(1) (2019) 108–117.
  • [21] Camacho E.F. and Bordons C. (Carlos), Model predictive control, Springer, 2007; pp 14-46.

Yıl 2020, Cilt: 41 Sayı: 2, 527 - 533, 25.06.2020

Yunis Torun , Sefa Malatyalı

https://doi.org/10.17776/csj.661666
Cited By: 2

Öz

Proje Numarası

M-737

Kaynakça

  • [1] Dai Y.Xue Y.and Zhang J., Condition monitoring based on sound feature extraction during bone drilling process, Proceedings of the 33rd Chinese Control Conference, (2014) 7317–7322.
  • [2] Chung G.B., Lee S.G., Kim S., Yi B.J., Kim W.K., Oh S.M., Kim Y.S., Park J.and Oh S.H., A robot-assisted surgery system for spinal fusion, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, (2005) 3744–3750.
  • [3] Torun Y., Ozturk A., Hatipoglu N.and Oztemur Z., Detection of Bone Excretion with Current Sensor in Robotic Surgery, UBMK 2018 - 3rd International Conference on Computer Science and Engineering, (2018) 185-189.
  • [4] Duan X., Al-Qwbani M., Zeng Y., Zhang W. and Xiang Z., Intramedullary nailing for tibial shaft fractures in adults, Cochrane Database of Systematic Reviews, (2012) CD008241.
  • [5] Osa T., Abawi C.F., Sugita N., Chikuda H., Sugita S., Tanaka T., Oshima H., Moro T., Tanaka S.and Mitsuishi M., Hand-Held Bone Cutting Tool with Autonomous Penetration Detection for Spinal Surgery, IEEE/ASME Transactions on Mechatronics, 20(6) (2015) 3018–3027.
  • [6] Torun Y., Ozturk A., Aksoz A. and Pazarci O., "Parameters Estimation of Orthopedic Drill," 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, (2019) 1–4.
  • [7] Carroll A. and Heiser G., An Analysis of Power Consumption in a Smartphone., USENIX annual technical conference, (2010) 271–285.
  • [8] Torun, Y., Öztürk, A., A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach., Ann Biomed Eng., 48 (2020) 1218–1229.
  • [9] Lee W.-Y. and Shih C.-L., Control and breakthrough detection of a three-axis robotic bone drilling system, Mechatronics, 2(16) (2006) 73–84.
  • [10] Farnworth G.H. and Burton J.A., Optimization of Drill Geometry for Orthopaedic Surgery, Proceedings of the Fifteenth International Machine Tool Design and Research Conference, (2015) 227–233.
  • [11] Deng Z., Jin H., Hu Y., He Y., Zhang P., Tian W.and Zhang J., Fuzzy force control and state detection in vertebral lamina milling, Mechatronics, 35 (2016) 1–10.
  • [12] Díaz I., Gil J.J.and Louredo M., Bone drilling methodology and tool based on position measurements, Computer Methods and Programs in Biomedicine, 2(112) (2013) 284–292
  • [13] Mayer M., Lin H.H., Peng Y.H., Lee P.Y.and Wang M.L., A drill signal detection technology for handheld medical drilling device, Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014, (2014) 958–961.
  • [14] Turner C.H. and Burr D.B., Basic biomechanical measurements of bone: A tutorial, Bone, 4(14) (1993) 595–608.
  • [15] Zioupos P., Currey J.D., Hamer A.J., The role of collagen in the declining mechanical properties of aging human cortical bone. J Biomed Mater Res. 45(2) (1999) 108-116.
  • [16] Radcliffe P. and Kumar D., Sensorless speed measurement for brushed DC motors, IET Power Electronics, 8(11) (2015) 2223–2228.
  • [17] Farrell M., Jackson J., Nielsen J., Bidstrup C. and McLain T., Error-State LQR Control of a Multirotor UAV, 2019 International Conference on Unmanned Aircraft Systems (ICUAS), (2019) 704–711.
  • [18] Nafea M., Ali A.R.M., Baliah J.and Ali M.S.M., Metamodel-based optimization of a PID controller parameters for a coupled-tank system, Telkomnika (Telecommunication Computing Electronics and Control), 16(4) (2018) 1590–1596.
  • [19] Samin R.E., Jie L.M.and Zawawi M.A., PID implementation of heating tank in mini automation plant using programmable logic controller (PLC), InECCE 2011 - International Conference on Electrical, Control and Computer Engineering, (2011) 515–519.
  • [20] Bai T.Li S. and Zheng Y., Distributed model predictive control for networked plant-wide systems with neighborhood cooperation, IEEE/CAA Journal of Automatica Sinica, 6(1) (2019) 108–117.
  • [21] Camacho E.F. and Bordons C. (Carlos), Model predictive control, Springer, 2007; pp 14-46.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Engineering Sciences
Yazarlar

Yunis Torun 0000-0002-6187-0451

Sefa Malatyalı 0000-0003-3761-0403

Proje Numarası M-737
Yayımlanma Tarihi 25 Haziran 2020
Gönderilme Tarihi 19 Aralık 2019
Kabul Tarihi 17 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 41 Sayı: 2

Kaynak Göster

APA Torun, Y., & Malatyalı, S. (2020). Power analysis of robotic medical drill with different control approaches. Cumhuriyet Science Journal, 41(2), 527-533. https://doi.org/10.17776/csj.661666

Cited By

Bone Drilling: Review with Lab Case Study of Bone Layer Classification Using Vibration Signal and Deep Learning Methods
Eng
https://doi.org/10.3390/eng5030083
Simulation of Disturbance Observer-Based Bone Tissue Change Prediction Approach for Orthopedic Drills
Türk Doğa ve Fen Dergisi
https://doi.org/10.46810/tdfd.1057096