TC25钛合金具有良好的高温强度和热稳定性,是制造航空发动机的理想材料。表面粗糙度是衡量钛合金零件表面加工质量的重要指标。基于正交试验数据,采用极差分析探究各铣削参数对铣削表面粗糙度的影响程度与影响规律。影响表面粗糙度的铣...TC25钛合金具有良好的高温强度和热稳定性,是制造航空发动机的理想材料。表面粗糙度是衡量钛合金零件表面加工质量的重要指标。基于正交试验数据,采用极差分析探究各铣削参数对铣削表面粗糙度的影响程度与影响规律。影响表面粗糙度的铣削参数的主次顺序为:每齿进给量f z>主轴转速n>轴向切深a p>径向切深a e。空列极差R=0.026,小于各因素极差值,说明各因素对指标均有较大影响且水平选择合理。采用多元线性回归方法建立了钛合金TC25铣削表面粗糙度预测模型。对预测模型进行了显著性检验,F=21.5473>F(4,11),说明模型高度显著。通过加工验证试验,证明了切削参数A 3 B 1 C 1 D 1为最优组合。验证试验的预测误差为1.2%~8.1%,证明预测模型具有较高的精度。展开更多
Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultr...Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials,the surface morphologies of grinding wheel were obtained firstly in the present work,the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated,the theoretical model for surface roughness was developed based on the above analysis.The prediction model was developed by using Gaussian processing regression(GPR)due to the influence of brittle fracture on machined surface roughness.In order to validate both the proposed theoretical and GPR models,32sets of experiments of ultrasonic vibration assisted grinding of BK7optical glass were carried out.Experimental results show that the average relative errors of the theoretical model and GPR prediction model are13.11%and8.12%,respectively.The GPR prediction results can match well with the experimental results.展开更多
文摘TC25钛合金具有良好的高温强度和热稳定性,是制造航空发动机的理想材料。表面粗糙度是衡量钛合金零件表面加工质量的重要指标。基于正交试验数据,采用极差分析探究各铣削参数对铣削表面粗糙度的影响程度与影响规律。影响表面粗糙度的铣削参数的主次顺序为:每齿进给量f z>主轴转速n>轴向切深a p>径向切深a e。空列极差R=0.026,小于各因素极差值,说明各因素对指标均有较大影响且水平选择合理。采用多元线性回归方法建立了钛合金TC25铣削表面粗糙度预测模型。对预测模型进行了显著性检验,F=21.5473>F(4,11),说明模型高度显著。通过加工验证试验,证明了切削参数A 3 B 1 C 1 D 1为最优组合。验证试验的预测误差为1.2%~8.1%,证明预测模型具有较高的精度。
基金Project(51375119) supported by the National Natural Science Foundation of China
文摘Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials,the surface morphologies of grinding wheel were obtained firstly in the present work,the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated,the theoretical model for surface roughness was developed based on the above analysis.The prediction model was developed by using Gaussian processing regression(GPR)due to the influence of brittle fracture on machined surface roughness.In order to validate both the proposed theoretical and GPR models,32sets of experiments of ultrasonic vibration assisted grinding of BK7optical glass were carried out.Experimental results show that the average relative errors of the theoretical model and GPR prediction model are13.11%and8.12%,respectively.The GPR prediction results can match well with the experimental results.
