磁性复合流体抛光氧化锆陶瓷的工艺优化

Process parameters optimization of zirconia ceramics polishing with magnetic compound fluid slurry

为提高氧化锆陶瓷工件的表面质量,采用磁性复合流体(由包含纳米级铁磁颗粒的磁流体与包含微米级羰基铁颗粒的磁流变液混合而成)对氧化锆陶瓷进行抛光,以达到降低材料表面粗糙度和减少表面与亚表面损伤的目的。利用田口方法设计3因素3水平正交试验,着重分析磁铁转速、加工间隙和抛光液磨粒粒径对表面粗糙度和材料去除率的影响规律,并采用方差分析法分析各因素对2个评价指标的影响权重。可达到最低表面粗糙度的工艺参数组合为:磁铁转速,300 r/min;加工间隙,0.5 mm;磨粒粒径,1.25μm。可达到最高材料去除率的工艺参数组合为:磁铁转速,400 r/min;加工间隙,0.5 mm;磨粒粒径,2.00μm。根据优化的工艺参数进行抛光,表面粗糙度最低可达4.5 nm,材料去除率最高可达0.117μm/min,优化效果显著。利用遗传算法优化BP神经网络建立抛光预测模型,预测误差为3.9484%。

In order to improve the surface quality of zirconia ceramic workpieces,the magnetic compound fluid polishing tool were utilized.This was done to lessen the material's surface roughness,minimize surface and subsurface damage.With a focus on the effects of magnet speed,processing gap,and abrasive particle size in the polishing fluid on surface roughness and material removal rate,a 3-factor,3-level orthogonal test was created using Taguchi's method.The weights of each factor on the two evaluation indices were then analyzed using ANOVA.The best process parameter combination for surface roughness was 300 r/min for the magnet speed,0.5 mm for the processing gap,and 1.25μm for the abrasive particle size;the best process parameter combination for material removal rate was 400 r/min for the magnet speed,0.5 mm for the processing gap,and 2μm for the abrasive particle size.With these processing parameters,the surface roughness can reach up to 4.5 nm,and the material removal rate can reach up to 0.117μm/min.The optimization effect is significant.The polishing prediction model was developed using a BP neural network that has been genetic algorithm optimized.The prediction error was 3.9484%.