Literature DB >> 24388102

Numerical simulation of bone screw induced pretension: the cases of under-tapping and conical profile.

Panagiotis E Chatzistergos1, Evangelos A Magnissalis2, Stavros K Kourkoulis3.   

Abstract

Even though screw induced pretension impacts the holding strength of bone screws, its implementation into the numerical simulation of the pullout phenomenon remains a problem with no apparent solution. The present study aims at developing a new methodology to simulate screw induced pretension for the cases of: (a) cylindrical screws inserted with under-tapping and (b) conical screws. For this purpose pullout was studied experimentally using synthetic bone and then simulated numerically. Synthetic bone failure was simulated using a bilinear cohesive zone material model. Pretension generation was simulated by allowing the screw to expand inside a hole with smaller dimensions or different shape than the screw itself. The finite element models developed here were validated against experimental results and then utilized to investigate the impact of under-tapping and conical angle. The results indicated that pretension can indeed increase a screw's pullout force but only up to a certain degree. Under-tapping increased cylindrical screws' pullout force up to 12%, 15% and 17% for synthetic bones of density equal to 0.08 g cm(-3), 0.16 g cm(-3) and 0.28 g cm(-3), respectively. Inserting a conical screw into a cylindrical hole increased pullout force up to 11%. In any case an optimum level of screw induced pretension exists.
Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cohesive material model; Damage simulation; Finite element analysis; Holding strength; Pedicle screw; Pullout; Synthetic bone

Mesh:

Year:  2013        PMID: 24388102     DOI: 10.1016/j.medengphy.2013.12.009

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  2 in total

1.  Osteosynthesis Metal Plate System for Bone Fixation Using Bicortical Screws: Numerical-Experimental Characterization.

Authors:  Andrea A R Olmos; Aureliano Fertuzinhos; Teresa D Campos; Isabel R Dias; Carlos A Viegas; Fábio A M Pereira; Nguyễn T Quyền; Marcelo F S F de Moura; Andrea Zille; Nuno Dourado
Journal:  Biology (Basel)       Date:  2022-06-20

2.  Pullout Strength Predictor: A Machine Learning Approach.

Authors:  Ravi Khatri; Vicky Varghese; Sunil Sharma; Gurunathan Saravana Kumar; Harvinder Singh Chhabra
Journal:  Asian Spine J       Date:  2019-06-03
  2 in total

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