A Novel Semiemperical Friction Coefficient Model Between Needle and Polyvinyl Alcohol Tissue Phantom and Its Validation by Using Computational Inverse Technique

Author:

Li Murong1,Lei Yong1,Hu Yingda1,Du Shilun1,Gao Dedong2,Wang Zhen1,Xu Tian1

Affiliation:

1. State Key Laboratory of Fluid Power and Mechatronic Systems;, Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

2. Department of Mechanical Engineering, Qinghai University, Qinghai 810016, China

Abstract

Abstract Needle–tissue interactive force is vital for developing a virtual simulation and planning system (SPS) and optimizing needle control strategy, wherein the friction coefficient is an important parameter but difficult to pre-determine. The existing studies on friction coefficient estimation were lack of qualitative analysis and did not consider the effect of normal pressure and interactive velocity, which may lead to the inaccuracy of the friction calculation. In this paper, we proposed a novel semiempirical friction coefficient model based on the modified classical elastic friction theory that constructs the relationship between the friction coefficient and parameters such as the normal pressure and velocity. The proposed friction coefficient model is validated by using the computational inverse technique based on coupled finite element material point (CFEMP) contact algorithm. The results show that the friction coefficient between the needle and polyvinyl alcohol (PVA) tissue phantom varies from 0.091 to 0.242 with different normal pressure (7.95–17.80 kPa) and insertion velocity (1–9 mm/s), which agrees well with the experimental data. The results of the paper can help to better understand the intrinsic characteristics of the needle–tissue interactions and optimal needle actuation strategies.

Publisher

ASME International

Subject

Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Novel Full Prediction Model of 3D Needle Insertion Procedures;Intelligent Robotics and Applications;2023

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