Skip to main content
SpringerLink
Log in

A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Robotic total hip arthroplasty is a procedure in which milling operations are performed on the femur to remove material for the insertion of a prosthetic implant. The robot performs the milling operation by following a sequential list of tool motions, also known as a toolpath, generated by a computer-aided manufacturing (CAM) software. The purpose of this paper is to explain a new toolpath force prediction algorithm that predicts cutting forces, which results in improving the quality and safety of surgical systems.

Methods

With a custom macro developed in the CAM system’s native application programming interface, cutting contact patch volume was extracted from CAM simulations. A time domain cutting force model was then developed through the use of a cutting force prediction algorithm. The second portion validated the algorithm by machining a hip canal in simulated bone using a CNC machine. Average cutting forces were measured during machining using a dynamometer and compared to the values predicted from CAM simulation data using the proposed method.

Results

The results showed the predicted forces matched the measured forces in both magnitude and overall pattern shape. However, due to inconsistent motion control, the time duration of the forces was slightly distorted. Nevertheless, the algorithm effectively predicted the forces throughout an entire hip canal procedure.

Conclusion

This method provides a fast and easy technique for predicting cutting forces during orthopedic milling by utilizing data within a CAM software.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Paul HA, Bargar WL, Mittestadt B, Musits B, Taylor R, Kazanzides P, Zuharas J, Williamson B, Hanson W (1992) Development of a surgical robot for cementless total hip arthroplasty. Clin Orthop Relat Res 285:57–66

    PubMed  Google Scholar 

  2. Denis K, Van Ham G, Vander Sloten J, Van Audekercke R, Van Der Perre G, De Schutter J, Kruth J-P, Bellemans J, Fabry G (2001) Influence of bone milling parameters on the temperature rise, milling forces and surface flatness in view of robot–assisted total knee arthroplasty. Int Congr Ser 1230:300–306

    Article  Google Scholar 

  3. Plaskos C (2003) Modelling and optimization of bone-cutting forces in orthopaedic surgery. Med Imaging Comput Comput Assist Interv 2878:254–261

    Google Scholar 

  4. Plaskos P, Cinquin A, Hodgson, Lavallée S (2005) Safety and accuracy considerations in developing a small sterilizable robot for orthopaedic surgery. In: IEEE conference on robototics and automation, pp 942–947

  5. Layegh S, Erdim H, Lazoglu I (2012) Offline force control and feedrate scheduling for complex free form surfaces in 5-axis milling. Proced CIRP 1.1:96–101

    Google Scholar 

  6. Lundskog J (1972) Heat and bone tissue. An experimental investigation of the thermal properties of bone tissue and threshold levels for thermal injury. Scand J Plast Reconstr Surger Suppl 9:1–80

    CAS  Google Scholar 

  7. Sugita N, Mitsuishi M (2009) Specifications for machining the bovine cortical bone in relation to its microstructure. J Biomech 42(16):2826–2829

    Article  PubMed  Google Scholar 

  8. Naohiko S, Yoshikazu N, Mamoru M (2008) Dynamic controlled milling process in bone cutting machine for orthopedic surgery. Trans Soc Instrum Control Eng 44.4:377–383

    Google Scholar 

  9. Kazanzides P, Zuhars J, Mittelstadt B, Taylor, Rusell H (1992) Force sensing and control for a surgical robot. In: IEEE international conference on robotics and automation

  10. Tunc L, Budak E (2009) Extraction of 5-axis milling conditions from CAM data for process simulation. Int J Adv Manuf Technol 43(5–6):538–550

    Article  Google Scholar 

  11. Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press, Cambridge

    Google Scholar 

  12. BoneSim—products and properties. BoneSim Laboratories. http://www.bonesim.com/products_and_properties

  13. Budak E, Altintas Y, Armarego EJA (1996) Prediction of milling force coefficients from orthogonal cutting data. J Manuf Sci Eng 118:216–224

    Article  Google Scholar 

  14. Inoue T, Kuramoto K, Nakashima Y, Sugita N, Mitsuishi M, Nakashima Y (2011) Surgical assistance system National University Corporation Tokyo University, Assignee. Patent 0306985

Download references

Acknowledgments

The authors express their appreciation to MTTRF and THINK Surgical, Inc., for lending their equipment and useful discussions. Support and funding for this work were provided by THINK Surgical, Inc.

Author information

Authors and Affiliations

  1. Advanced Research for Manufacturing Systems Laboratory, Department of Mechanical and Aerospace Engineering, University of California Davis, One Shields Ave, Davis, CA, 95616, USA

    Babak Kianmajd, David Carter & Masakazu Soshi

Authors
  1. Babak Kianmajd
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masakazu Soshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masakazu Soshi.

Ethics declarations

Conflict of interest

The authors declare they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kianmajd, B., Carter, D. & Soshi, M. A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty. Int J CARS 11, 1871–1880 (2016). https://doi.org/10.1007/s11548-016-1355-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1355-x

Keywords

Search

Navigation