A New Model for Predicting Neurosurgery Skull Bone Grinding Temperature Field

doi:10.3901/JME.2018.23.215

Abstract

Abstract: The temperature field theory based on the steady heat flux density has a large error between the calculated value and the actual temperature value, which is the bottleneck of the theoretical study of the grinding temperature field. The convective heat transfer coefficient and thermal conductivity model in materials is established firstly in this paper, and the dynamic heat flux model is established by using the high order Gauss function fitting to acquire the dynamic grinding force in real-time, on the basis of which, a new prediction model of neurosurgery bone grinding temperature field is established. The numerical analysis of the grinding temperature field is carried out under dry grinding, mist and nanoparticle jet mist cooling. The mechanical property of fresh bovine femur is closest to the human skull bone, and then the verification grinding experiments of fresh bovine femurs are carried out using hydroxyapatite nanoparticles and normal saline. The results show that, compared with the experimentally measured temperature, model error loading steady heat flux is 18.8%, and model error using the new model is 6.6%. The theoretical analysis of the new model is consistent with the experimental results, that is, the new prediction model of the bone grinding temperature field is more in line with the actual conditions.

Key words: dynamic heat flux, hydroxyapatite, mist cooling, neurosurgery skull grinding, temperature field simulation

CLC Number:

TG580

YANG Min, LI Changhe, ZHANG Yanbin, JIA Dongzhou, ZHANG Xianpeng, LI Runze. A New Model for Predicting Neurosurgery Skull Bone Grinding Temperature Field[J]. Journal of Mechanical Engineering, 2018, 54(23): 215-222.

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