氧化锆陶瓷车削刀具几何参数的多目标优化

通过氧化锆车削试验测得切削力和刀具磨损量，以工件材料去除量与刀具磨损量的比值作为刀具利用率的量化指标.采用粒子群算法改进BP神经网络，并以此对单因素试验值进行训练预测.采用最小二乘拟合，建立刀具利用率和切削力关于各刀具几何参数的一元模型，以相关系数检验模型的可靠性.基于一元模型，分别提出了刀具利用率和切削力关于刀具几何参数的多元模型.利用粒子群算法结合正交试验值对多元模型进行优化求解，并通过验证试验证明了多元模型具有较高的精度.将多元模型作为目标函数，以刀具利用率最大和切削力最小为优化目标，基于粒子群算法进行了刀具几何参数的多目标优化，验证试验结果表明优化得到的刀具几何参数是合理的.

Multi-objective Optimization of Tool Geometry Parameters in Turning Zirconia Ceramics

The cutting force and tool wear were measured through the zirconia turning experiment， and the ratio of workpiece material removal to tool wear was as a quantitative index of tool utilization. The single-factor experimental values were trained and predicted by BP neural network that 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， and the reliability of the models was tested by the correlation coefficient. The multivariate models based on the one-dimensional models are proposed too. The multivariate models were solved by PSO combined with orthogonal experimental values， and was proved to be more accurate through experiments. Taking the multivariate models as the objective function and the maximum tool utilization/minimum cutting force as the optimization goals， the tool geometry parameters were optimized by PSO， and the experiments show that the optimized tool geometry parameters are reasonable.