| Engineering ceramic materials have been widely applied in the fields of military industry,aerospace and biomedicine due to their stable chemical properties and excellent mechanical properties.However,ceramics have the material properties of hardness,brittleness and inhomogeneity of material structure,it leads to large cutting force,low tool life,difficult machining and large machining damage.The high quality and efficient processing of ceramic materials is seriously hindered by these machining defects.The accurate and effective realization of turning process monitoring and process parameter optimization has always been an important issue to improve turning quality and efficiency.The optimization of tool utilization and tool geometric parameters in turning zirconia ceramics is selected as the research object.The research has been carried out in the following aspects:the formation mechanism of turning adhesion effect,the theoretical modeling of tool utilization rate,the influence of tool geometric parameters on tool utilization rate and the multi-objective optimization of tool geometric parameters.The main work is summarized as follows:(1)The tool-to-piece cutting process was investigated from a microscopic point of view.Based on the strength theory and the essence of tool adhesive wear material migration,the adhesion effect mechanism of turning zirconia ceramics was studied.(2)Based on the mechanism of tool-to-piece adhesion effect,the ratio of workpiece material removal to tool wear was as a quantitative index of tool utilization.Through mechanical analysis,using layer-by-layer integration method,combined with the machinability of workpiece materials,the relationship between tool geometric parameters and flank adhesive wear area is synthesized,and the theoretical model of tool utilization rate is established.(3)Through the experiment of turning zirconia ceramics with YG6 tool,the rationality of the theoretical model is verified,and the effects of tool rake angle,back angle and corner radius on tool utilization are studied.Verification experiment results showed that tool utilization increases firstly and decreases afterward with the increase of rake angle and back angle,while increases with the increase of the corner radius,and found that the influence of rake angle and back angle on tool utilization is more significant than the corner radius.The theoretical value of the tool volume wear is consistent with the experimental results,and the average relative error is 5%～15%,which indicates that the model has high reliability.(4)The single-factor experimental values were trained and predicted by BP neural network which was improved by particle swarm optimization(PSO).The one-dimensional models describing the relationship of tool utilization/cutting force and the geometric parameters of each tool were established by least-squares fitting.The reliability of the models was tested by the decision coefficient,and all above 0.96.The multivariate models based on the one-dimensional models are proposed respectively.The multivariate models were solved by PSO combined with orthogonal experimental values.The experimental results show that the errors between the experimental value and the predicted value of the model are less than 10%,indicating that the multivariate model has high accuracy.(5)Taking the multivariate models as the objective function,and the maximum tool utilization/minimum cutting force as the optimization goals.The weights between goals were adjusted by equivalent dynamic weights.The tool geometry parameters were optimized by PSO and the experiments show that the optimized tool geometry parameters are reasonable. |
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