Influence of Cooling and Lubrication Parameters on Robot Bone Grinding Temperature and Prediction Modeling

Abstract

In the process of robot bone grinding, a large amount of heat is generated, which will cause mechanical and thermal damage to bone tissues and nerves. It is necessary to study the influence of cooling and lubrication parameters on the robot bone grinding temperature and establish the prediction models among them. The FE model of single abrasive bone grinding was established to explore the influence of cooling and lubrication parameters on the bone grinding temperature. Response surface design of experiment was carried out to obtain the simulation results, and Design-Expert was used to establish a multiple regression prediction models of grinding temperature under the condition of different cooling and lubrication. Through the variance and response surface comparative analysis of the prediction model, the influence rules of the bone grinding parameters and the cooling and lubrication parameters on the bone grinding temperature was obtained. A robot bone grinding experiment was performed to prove the accuracy of FE simulation and prediction model. The research results show that the relationship between grinding temperature and cooling lubrication parameters obtained by FE simulation, RSM prediction and experiment verification is consistent, and the simulation model and prediction model of cooling and lubrication parameters are proven to be correct and effective. The influence rules and prediction effects obtained in this study will provide a reasonable scheme for doctors to implement robot bone grinding with high efficiency and low damage, and establish the theoretical basis for the effective control of robot bone grinding force thermal damage.

Optimization of Laminar Flow in Non-Circular Ducts: A Comprehensive CFD Analysis

“…As a result of statistical behavior, the P-Values and F-Values for mesh size (A), Re-Number (B), and type of duct (C) show which parameters are important and which are not important in this study based on the ANOVA [71][72][73]. The final results of each simulation run represented by the PF response in Table 6 are inputted into the statistical analysis based on regression and ANOVA, where FIT and RES for this response are observed in Table 6 as poly-fit and residual values, respectively.…”