مدل‌سازی رفتار نیرو در فرآیند فرزکاری اتوماتیک استخوان کورتیکال گاو با استفاده از سیستم عصبی- فازی تطبیقی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 صنعتی اراک-مهندسی مکانیک

2 مهندسی مکانیک، دانشگاه صنعتی اراک

3 دانشگاه صنعتی اراک*مکانیک

چکیده

در این مقاله، یک سیستم استنتاج عصبی- فازی تطبیقی برای مدل‌سازی اثر پارامترهای مهم در فرزکاری استخوان کورتیکال شامل سرعت دورانی ابزار، نرخ پیشروی، عمق برش و قطر ابزار برای پیش‌بینی نیروهای برش مورد استفاده قرار گرفته است. به منظور مدلسازی رفتار نیروی فرآیند، آزمایش‌های تجربی بر روی استخوان تازه ران گاو صورت پذیرفته است. سپس از نتایج آزمایش‌های انجام‌شده برای آموزش و تست سیستم استنتاج، بهره گرفته شده است. در این مدل مهمترین پارامترهای فرزکاری اتوماتیک استخوان کورتیکال شامل سرعت دورانی ابزار، نرخ پیشروی، قطر ابزار و عمق برش به عنوان پارامترهای ورودی و نیروهای برش در سه جهت پیشروی، عمود بر پیشروی و عمود بر سطح استخوان و همچنین نیروی برآیند به عنوان خروجی در نظر گرفته شده‌اند. در این راستا، سیستم استنتاج عصبی- فازی تطبیقی بر مبنای 75 درصد از داده‌‌های آزمایشگاهی آموزش داده شده و از 25 درصد داده‌‌های باقیمانده به منظور تست درستی مدل بدست‌آمده استفاده شده است. دقت مدل بدست‌آورده‌شده با استفاده از نمودارهای مختلف و همچنین معیارهای آماری متعددی بررسی شده است. از نتایج بدست‌آمده مشخص می‌شود که شبکه عصبی-فازی تطبیقی در پیش‌بینی نیروهای برش در فرآیند سوراخکاری استخوان کورتیکال بسیار موفق عمل کرده‌است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Process modeling of force behavior in the automatic bovine cortical bone milling process using adaptive neuro-fuzzy inference system

نویسندگان [English]

  • Vahid Tahmasbi 1
  • Amir Hossein Rabiee 2
  • Mahdi Safari 3
1 صنعتی اراک-مهندسی مکانیک
2 mechanical engineering, arak university of technology
3 Department of Mechanical Engineering/Arak University of Technology
چکیده [English]

In this article, an adaptive neuro-fuzzy inference system is utilized to model the effect of important parameters in the cortical bone milling process including the rotational speed, feed rate, depth of cut and tool diameter to predict the cutting forces. To model the process force behavior, experimental tests are conducted on the fresh cow femur. Next, the results of performed experiments are used to train and test the inference system. In this model, the most influential parameters of automatic cortical bone milling process including the rotational speed, feed rate, tool diameter and depth of cut are taken as the input parameters, while the cutting forces in the feed direction, normal to the feed direction and normal to the bone surface as well as the resultant force are considered as the output. To this aim, the adaptive neuro-fuzzy inference system relies on 75% of the trained laboratory data and the remaining 25% to test the model validation. The accuracy of the obtained model is investigated using different diagrams and numerous statistical criteria. The results indicate that the adaptive neuro-fuzzy network has shown a successful performance in predicting the cutting forces of cortical bone milling process.

کلیدواژه‌ها [English]

  • bone milling
  • cortical bone
  • machining
  • Neuro-fuzzy network
  • bone cutting forces
 [1] A.A.o.O. Surgeons, Total Knee Replacement, in, 2015.
[2] T.P. James, G. Chang, S. Micucci, A. Sagar, E.L. Smith, C. Cassidy, Effect of applied force and blade speed on histopathology of bone during resection by sagittal saw, Medical engineering & physics, 36(3) (2014) 364-370.
 [3] M.J. Fox, J.M. Scarvell, P.N. Smith, S. Kalyanasundaram, Z.H. Stachurski, Lateral drill holes decrease strength of the femur: an observational study using finite element and experimental analyses, Journal of orthopaedic surgery and research, 8 (2013) 29.
[4] V. Tahmasbi, M. Ghoreishi, M.J.P.o.t.I.o.M.E. Zolfaghari, Part H: Journal of Engineering in Medicine, Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone, 231(11) (2017) 1012-1024.
[5] B.L. Tai, L. Zhang, A. Wang, S. Sullivan, A.J. Shih, Neurosurgical Bone Grinding Temperature Monitoring, Procedia CIRP, 5 (2013) 226-230.
[6] M. Marco, M. Rodríguez-Millán, C. Santiuste, E. Giner, M. Henar Miguélez, A review on recent advances in numerical modelling of bone cutting, Journal of the Mechanical Behavior of Biomedical Materials, 44 (2015) 179-201.
[7] T. Cao, X. Li, Z. Gao, G. Feng, P. Shen, A method for identifying otological drill milling through bone tissue wall, The international journal of medical robotics + computer assisted surgery : MRCAS, 7(2) (2011) 148-155.
[8] J.H. Lonner, Robotically Assisted Unicompartmental Knee Arthroplasty with a Handheld Image-Free Sculpting Tool, Operative Techniques in Orthopaedics, 25(2) (2015) 104-113.
[9] C. Natali, P. Ingle, J. Dowell, Orthopaedic bone drills-can they be improved? Temperature changes near the drilling face, The Journal of bone and joint surgery. British volume, 78(3) (1996) 357-362.
[10] R.K. Pandey, S.S. Panda, Drilling of bone: A comprehensive review, Journal of clinical orthopaedics and trauma, 4(1) (2013) 15-30.
[11] K. Denis, G. Van Ham, J. Vander Sloten, R. Van Audekercke, G. Van der Perre, J. De Schutter, J.P. Kruth, J. Bellemans, G. Fabry, Influence of bone milling parameters on the temperature rise, milling forces and surface flatness in view of robot-assisted total knee arthroplasty, International Congress Series, 1230 (2001) 300-306.
[12] W. Wang, Y. Shi, N. Yang, X. Yuan, Experimental analysis of drilling process in cortical bone, Medical engineering & physics, 36(2) (2014) 261-266.
[13] M. Arbabtafti, M. Moghaddam, A. Nahvi, M. Mahvash, B. Richardson, B. Shirinzadeh, Physics-Based Haptic Simulation of Bone Machining, IEEE Transactions on Haptics, 4(1) (2011) 39-50.
[14] M. Moghaddam, A. Nahvi, M. Arbabtafti, M. Mahvash, A Physically Realistic Voxel-Based Method for Haptic Simulation of Bone Machining, in: M. Ferre (Ed.) Haptics: Perception, Devices and Scenarios, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 651-660.
[15] B. Kianmajd, D. Carter, M. Soshi, A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty, International journal of computer assisted radiology and surgery, 11(10) (2016) 1871-1880.
[16] C. Plaskos, Modeling and Design of Robotized Tools and Milling Techniques for Total Knee Arthroplasty, 2005.
[17] D. Wu, L. Zhang, S. Liu, Research on establishment and validation of cutting force prediction model for bone milling, in:  2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015, pp. 1864-1869.
[18] G. Van Ham, K. Denis, J. Vander Sloten, R. Van Audekercke, G. Van der Perre, J. De Schutter, E. Aertbelien, S. Demey, J. Bellemans, Machining and accuracy studies for a tibial knee implant using a force-controlled robot, Computer aided surgery : official journal of the International Society for Computer Aided Surgery, 3(3) (1998) 123-133.
[19] T. Inoue, N. Sugita, M. Mitsuishi, T. Saito, Y. Nakajima, Y. Yokoyama, K. Fujiwara, N. Abe, T. Ozaki, M. Suzuki, K. Kuramoto, Y. Nakashima, K. Tanimoto, Optimal control of cutting feed rate in the robotic milling for total knee arthroplasty, in:  2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, 2010, pp. 215-220.
[20] P.A. Federspil, B. Plinkert, P.K. Plinkert, Experimental robotic milling in skull-base surgery, Computer aided surgery : official journal of the International Society for Computer Aided Surgery, 8(1) (2003) 42-48.
[21] N. Sugita, F. Genma, Y. Nakajima, M. Mitsuishi, Adaptive Controlled Milling Robot for Orthopedic Surgery, in:  Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007, pp. 605-610.
[22] C. Plaskos, A.J. Hodgson, P. Cinquin, Modelling and Optimization of Bone-Cutting Forces in Orthopaedic Surgery, in: R.E. Ellis, T.M. Peters (Eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003, Springer Berlin Heidelberg, Berlin, Heidelberg, 2003, pp. 254-261.
[23] c. plaskos, bone sawing and milling in computer-assisted total knee arthroplasty, university of western ontario,  (1999).
[24] Y. Hu, H. Jin, L. Zhang, P. Zhang, J. Zhang, State Recognition of Pedicle Drilling With Force Sensing in a Robotic Spinal Surgical System, IEEE/ASME Transactions on Mechatronics, 19(1) (2014) 357-365.
[25] Y. Dai, Y. Xue, J. Zhang, Vibration-Based Milling Condition Monitoring in Robot-Assisted Spine Surgery, IEEE/ASME Transactions on Mechatronics, 20(6) (2015) 3028-3039.
[26] Z. Deng, H. Jin, Y. Hu, Y. He, P. Zhang, W. Tian, J. Zhang, Fuzzy force control and state detection in vertebral lamina milling, Mechatronics, 35 (2016) 1-10.
[27] H. Jin, Y. Hu, Z. Deng, P. Zhang, Z. Song, J. Zhang, Model-based state recognition of bone drilling with robotic orthopedic surgery system, in:  2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, pp. 3538-3543.
[28] C.-T. Lin, C.G. Lee, C.-T. Lin, C. Lin, Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems, Prentice hall PTR Upper Saddle River NJ, 1996.
[29] J.M. Zurada, Introduction to artificial neural systems, West publishing company St. Paul, 1992.
[30] D. Nauck, F. Klawonn, R. Kruse, Foundations of neuro-fuzzy systems, John Wiley & Sons, Inc., 1997.
[31] J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3) (1993) 665-685.
[32] I. Maher, M. Eltaib, A.A. Sarhan, R. El-Zahry, Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling, The International Journal of Advanced Manufacturing Technology, 74(1-4) (2014) 531-537.
[33] S.-P. Lo, An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling, Journal of Materials Processing Technology, 142(3) (2003) 665-675.
[34] I. Shivakoti, G. Kibria, P.M. Pradhan, B.B. Pradhan, A. Sharma, ANFIS based prediction and parametric analysis during turning operation of stainless steel 202, Materials and Manufacturing Processes, 34(1) (2019) 112-121.
[35] K. Alam, A.V. Mitrofanov, V.V. Silberschmidt, Experimental investigations of forces and torque in conventional and ultrasonically-assisted drilling of cortical bone, Medical engineering & physics, 33(2) (2011) 234-239.
[36] G. Singh, V. Jain, D. Gupta, A. Ghai, Optimization of process parameters for drilled hole quality characteristics during cortical bone drilling using Taguchi method, Journal of the mechanical behavior of biomedical materials, 62 (2016) 355-365.
[37] D. Vashishth, K. Tanner, W. Bonfield, Contribution, development and morphology of microcracking in cortical bone during crack propagation, Journal of Biomechanics, 33(9) (2000) 1169-1174.
[38] R.K. Pandey, S. Panda, Multi-performance optimization of bone drilling using Taguchi method based on membership function, Measurement, 59 (2015) 9-13.
[39] K. Alam, A. Mitrofanov, V.V. Silberschmidt, Experimental investigations of forces and torque in conventional and ultrasonically-assisted drilling of cortical bone, Medical engineering & physics, 33(2) (2011) 234-239.
[40] G. Augustin, S. Davila, K. Mihoci, T. Udiljak, D.S. Vedrina, A. Antabak, Thermal osteonecrosis and bone drilling parameters revisited, Archives of Orthopaedic and Trauma Surgery, 128(1) (2008) 71-77.
[41] R.K. Pandey, S. Panda, Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach, Journal of Intelligent Manufacturing, 26(6) (2015) 1121-1129.
[42] G. Augustin, S. Davila, K. Mihoci, T. Udiljak, D.S. Vedrina, A. Antabak, Thermal osteonecrosis and bone drilling parameters revisited, Archives of orthopaedic and trauma surgery, 128(1) (2008) 71-77.
[43] R.K. Pandey, S.S. Panda, Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach, Journal of Intelligent Manufacturing, 26(6) (2015) 1121-1129.
[44] T. Varol, S. Ozsahin, Artificial neural network analysis of the effect of matrix size and milling time on the properties of flake Al-Cu-Mg alloy particles synthesized by ball milling, Particulate Science and Technology, 37(3) (2019) 381-390.
[45] M. Kubat, Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7, The Knowledge Engineering Review, 13(4) (1999) 409-412.