Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 3: 61 - 61

DOI: https://doi.org/10.1186/s10033-022-00744-x

Special Issue on Processing of Biological Tissue

Bone Milling: On Monitoring Cutting State and Force Using Sound Signals

  • Zhenzhi Ying ,
  • Liming Shu ,
  • Naohiko Sugita
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  • 1. Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan;
    2. Research into Artifacts Center for Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan

收稿日期: 2021-06-01

  修回日期: 2022-04-23

  网络出版日期: 2022-10-24

基金资助

Supported by the Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology (Grant No. DMETKF2020004)

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Bone Milling: On Monitoring Cutting State and Force Using Sound Signals

  • Zhenzhi Ying ,
  • Liming Shu ,
  • Naohiko Sugita
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  • 1. Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan;
    2. Research into Artifacts Center for Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1138656, Japan

Received date: 2021-06-01

  Revised date: 2022-04-23

  Online published: 2022-10-24

Supported by

Supported by the Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology (Grant No. DMETKF2020004)

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摘要

Efficient monitoring of bone milling conditions in orthopedic and neurosurgical surgery can prevent tissue, bone, and tool damage, and reduce surgery time. Current researches are mainly focused on recognizing the cutting state using force signal. However, the force signal during the milling process is difficult and expensive to acquire. In this study, a neural network-based method is proposed to recognize the cutting state and force during the bone milling process using sound signals. Numerical modeling of the cutting force is performed to capture the relationship between the cutting force and the depth of cut in the bone milling process. The force model is used to calibrate the training data to improve the recognition accuracy. Wavelet package transform is used for signal processing to understand bone-cutting phenomena using sound signals. The proposed system succeeds to monitor the bone milling process to reduce the surgical risk. Experiments on standard bone specimens and vertebrae also indicate that the proposed approach has considerable potential for use in computer-assisted and robot-assisted bone-cutting systems used in various types of surgery.

关键词： Cutting state; Force; Milling; Artificial neural network

本文引用格式

Zhenzhi Ying , Liming Shu , Naohiko Sugita . Bone Milling: On Monitoring Cutting State and Force Using Sound Signals[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(3) : 61 -61 . DOI: 10.1186/s10033-022-00744-x

Abstract

Efficient monitoring of bone milling conditions in orthopedic and neurosurgical surgery can prevent tissue, bone, and tool damage, and reduce surgery time. Current researches are mainly focused on recognizing the cutting state using force signal. However, the force signal during the milling process is difficult and expensive to acquire. In this study, a neural network-based method is proposed to recognize the cutting state and force during the bone milling process using sound signals. Numerical modeling of the cutting force is performed to capture the relationship between the cutting force and the depth of cut in the bone milling process. The force model is used to calibrate the training data to improve the recognition accuracy. Wavelet package transform is used for signal processing to understand bone-cutting phenomena using sound signals. The proposed system succeeds to monitor the bone milling process to reduce the surgical risk. Experiments on standard bone specimens and vertebrae also indicate that the proposed approach has considerable potential for use in computer-assisted and robot-assisted bone-cutting systems used in various types of surgery.

Key words： Cutting state; Force; Milling; Artificial neural network

参考文献

[1] Z Chen, Y Zhang, C Wang, et al. Understanding the cutting mechanisms of composite structured soft tissues. Int. J. Mach. Tools Manuf., 2021, 161: 103685.
[2] J A Robles-Linares, D Axinte, Z Liao, et al. Machining-induced thermal damage in cortical bone: Necrosis and micro-mechanical integrity. Mater. Des., 2021, 197: 109215.
[3] Z Jiang, X Qi, Y Sun, et al. Cutting depth monitoring based on milling force for robot-assisted laminectomy. IEEE Trans. Autom. Sci. Eng., 2020, 17(1): 2–14.
[4] Z Ying, L Shu, N Sugita. Autonomous penetration perception for bone cutting during laminectomy. 2020 IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, 2020, 21(1): 1–9.
[5] N Sugita, T Nakano, T Kato, et al. Tool path generator for bone machining in minimally invasive orthopedic surgery. IEEE/ASME Trans. Mechatronics, 2010, 15(3): 471–479.
[6] D Hill, T Williamson, C Y Lai, et al. Robots and tools for remodeling bone. IEEE Rev. Biomed. Eng., 2020, 13: 184–198.
[7] Q Li, Z Du, H Yu. Grinding trajectory generator in robot-assisted laminectomy surgery. Int. J. Comput. Assist. Radiol. Surg., 2021, 16(3): 485–494.
[8] Liming Shua, Shihao Lia, Makoto Terashima, et al. A novel self-centring drill bit design for low-trauma bone drilling. Int. J. Mach. Tools Manuf., 2020, 154: 103568.
[9] Y Sun, L Wang, Z Jiang, et al. State recognition of decompressive laminectomy with multiple information in robot-assisted surgery. Artif. Intell. Med., 2020, 102: 101763.
[10] K I A lateef Al-Abdullah, H Abdi, C P Lim, et al. Force and temperature modelling of bone milling using artificial neural networks. Meas. J. Int. Meas. Confed., 2017, 116: 25–37.
[11] N Crawford, N Johnson, N Theodore. Ensuring navigation integrity using robotics in spine surgery. J. Robot. Surg., 2020, 14(1): 177–183.
[12] Z Liao, D A Axinte. On monitoring chip formation, penetration depth and cutting malfunctions in bone micro-drilling via acoustic emission. J. Mater. Process. Technol., 2016, 229: 82–93.
[13] D Wu, C Jennings, J Terpenny, et al. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J. Manuf. Sci. Eng., 2017, 139: 7.
[14] D M D'Addona, A M M S Ullah, D Matarazzo. Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J. Intell. Manuf., 2017, 28(6): 1285–1301.
[15] Z Ying, L Shu, T Kizaki, et al. Hybrid approach for onsite monitoring and anomaly detection of cutting tool life. Procedia CIRP, 2021, 104: 1541–1546.
[16] Y Dai, Y Xue, J Zhang, et al. Biologically-inspired auditory perception during robotic bone milling. 2017 IEEE Int. Conf. Robot. Autom., 2017: 1112–1116.
[17] F Guan, Y Sun, X Qi, et al. State recognition of bone drilling based on acoustic emission in pedicle screw operation. Sensors, 2018, 18: 5.
[18] Z Deng, Haiyang Jin, Ying Hu, et al. Fuzzy force control and state detection in vertebral lamina milling. Mechatronics, 2016, 35: 1–10.
[19] S Y Nottestad, J J Baumel, D B Kimmel, et al. The proportion of trabecular bone in human vertebrae. J. Bone Miner. Res., 1987, 2(3): 221–229.
[20] R Xu, A Burgar, N A Ebraheim, et al. The quantitative anatomy of the laminas of the spine. Spine, 1999, 24(2): 107–113.
[21] N Sugita, F Genma, Y Nakajima, et al. Toolpath optimization for a milling robot of minimally invasive orthopedic surgery. Proceedings - IEEE International Conference on Robotics and Automation, 2007: 2273–2278.
[22] A Lamikiz, L N L De Lacalle, J A Sánchez, et al. Cutting force estimation in sculptured surface milling. Int. J. Mach. Tools Manuf., 2004, 44(14): 1511–1526.
[23] A Lamikiz, L N Lopez De Lacalle, J A Sanchez, et al. Calculation of the specific cutting coefficients and geometrical aspects in sculptured surface machining. Mach. Sci. Technol., 2005, 9(3): 411–436.
[24] K. Denis et al., 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, 2001, 1230: 300–306.
[25] Z Jiang, X Qi, Y Sun, et al. Cutting depth monitoring based on milling force for robot-assisted laminectomy. IEEE Trans. Autom. Sci. Eng., 2019: 1–13.
[26] C Sun, Chi Zhang, X Chen, et al. Deep residual network with hybrid dilated convolution for gearbox fault diagnosis. Proceedings of the International Symposium on Flexible Automation, 2018: 318–324.
[27] M Zhao, M Kang, B Tang, et al. Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Trans. Ind. Electron., 2018, 65(5): 4290–4300.
[28] S Elanayar, Y C Shin. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Trans. Neural Networks, 1994, 5(4): 594–603.
[29] J Aerssens, S Boonen, G Lowet, et al. Interspecies differences in bone composition, density, and quality: Potential implications for in vivo bone research. Endocrinology, 1998, 139(2): 663–670.
[30] M Clerc. Particle swarm optimization, part. Swarm Optim., 2010: 1942–1948.
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