Research Papers

A New Methodology of Finding Optimal Toolpath and Tooling Strategies for Robotic-Assisted Arthroplasty

[+] Author and Article Information
Babak Kianmajd

ARMS Laboratory,
Department of Mechanical and
Aerospace Engineering,
University of California, Davis,
Davis, CA 95616 
e-mail: BBKianmajd@ucdavis.edu

Masakazu Soshi

ARMS Laboratory,
Department of Mechanical
and Aerospace Engineering,
University of California, Davis,
Davis, CA 95616 
e-mail: MSoshi@ucdavis.edu

Manuscript received June 8, 2016; final manuscript received October 16, 2016; published online December 21, 2016. Assoc. Editor: Rita M. Patterson.

J. Med. Devices 11(1), 011006 (Dec 21, 2016) (7 pages) Paper No: MED-16-1236; doi: 10.1115/1.4035129 History: Received June 08, 2016; Revised October 16, 2016

Robotic total hip arthroplasty is a procedure in which a milling operation is performed on the femur followed by insertion of a prosthetic implant. Although surgeons operate the robots, they do not control the choice of robotic tools and cutting strategies of the robot. Toolpath parameters, such as feedrate, tool geometry, and spindle speeds, govern the cutting forces of the robot. This research covers a methodological approach for finding optimal parameters such that cutting forces and surgical times are reduced. Many different types of orthopedic surgical burs were retrofitted into an advanced computer numerically controlled (CNC) machine, and the characteristics of each tool were evaluated. A simulation cutting model was then developed to find the parameters that could remove the most material in the fastest amount of time without violating any of the safety constraints of surgery. The new methodology proposed not only finds the theoretical optimal parameters but also expedites the process of finding sufficient parameters for orthopedic surgery.

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Fig. 1

The TSolution One System for Total Hip Arthroplasty (THINK Surgical, Fremont, CA)

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Fig. 3

Flowchart summarizing the calculation of time-domain cutting forces in milling

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Fig. 2

Illustration of chip load formation from an end mill [8]

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Fig. 5

Experimental tool selection

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Fig. 6

Tesing experiments cut into BoneSim [10]

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Fig. 7

Experimental setup to find cutting coefficients

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Fig. 4

Summary of methodology to optimize tool and toolpath parameters

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Fig. 8

Average forces in the X direction as a function of chip load

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Fig. 9

Average forces in the Y direction as a function of chip load

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Fig. 10

Experimental and measured cuts in side milling from tool 4

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Fig. 11

Experimental and measured cuts in side milling from tool 8

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Fig. 12

The bar graph shows each tool's achievable maximum material removal rate that corresponds to their respective optimal tool paths

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Fig. 13

Each tools respective average force and power simulated with the optimal toolpath parameters



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