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
The wall surface roughness renders a significant impact on ventilation of roadways and cross-sectional wind speed distribution.Herein,the wall roughness(Ra)in the roadway has been defined theoretically.Moreover,three-...The wall surface roughness renders a significant impact on ventilation of roadways and cross-sectional wind speed distribution.Herein,the wall roughness(Ra)in the roadway has been defined theoretically.Moreover,three-center arched roadway models for different situations are established based on the normal distribution of roof roughness.The influence of inlet velocity,roof roughness and roadway height on wind speed distribution is systematically studied by using Fluent software.At Ra=0.1 m,the simulation results reveal that the wind speed is negatively related to the distance from the wall to the point where 80%of the central wind speed is reached(DA).Also,the wind speed distribution is significantly influenced by increasing the roof roughness.However,the wind speed distribution becomes asymmetric at Ra=0.2 m and 0.3 m.Furthermore,the low-speed area(v≤1 m/s)started to concentrate on the roof with the increase of roadway height.Overall,an Ra value of<0.1 m can reduce the influence of wall roughness on wind speed distribution of the roadway,which is suggested in practical applications.展开更多
The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was ...The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was presented and then the experimental setup of PECF system was established.The Taguchi method was introduced to assess the effect of finishing parameters on the gear tooth surface roughness,and the training data was also obtained through experiments.The comparison between the predicted values and the experimental values under the same conditions was carried out.The results show that the predicted values are found to be approximately consistent with the experimental values.The mean absolute percent error (MAPE) is 2.43% for the surface roughness and 2.61% for the applied voltage.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.
基金Project(2017YFC0602901)supported by the National Key Research and Development Program of ChinaProject(2019zzts988)supported by the Postgraduate Independent Exploration and Innovative Project of Central South University,China。
文摘The wall surface roughness renders a significant impact on ventilation of roadways and cross-sectional wind speed distribution.Herein,the wall roughness(Ra)in the roadway has been defined theoretically.Moreover,three-center arched roadway models for different situations are established based on the normal distribution of roof roughness.The influence of inlet velocity,roof roughness and roadway height on wind speed distribution is systematically studied by using Fluent software.At Ra=0.1 m,the simulation results reveal that the wind speed is negatively related to the distance from the wall to the point where 80%of the central wind speed is reached(DA).Also,the wind speed distribution is significantly influenced by increasing the roof roughness.However,the wind speed distribution becomes asymmetric at Ra=0.2 m and 0.3 m.Furthermore,the low-speed area(v≤1 m/s)started to concentrate on the roof with the increase of roadway height.Overall,an Ra value of<0.1 m can reduce the influence of wall roughness on wind speed distribution of the roadway,which is suggested in practical applications.
基金Project(90923022) supported by the National Natural Science Foundation of ChinaProject(2009220022) supported by Liaoning Science and Technology Foundation,China
文摘The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was presented and then the experimental setup of PECF system was established.The Taguchi method was introduced to assess the effect of finishing parameters on the gear tooth surface roughness,and the training data was also obtained through experiments.The comparison between the predicted values and the experimental values under the same conditions was carried out.The results show that the predicted values are found to be approximately consistent with the experimental values.The mean absolute percent error (MAPE) is 2.43% for the surface roughness and 2.61% for the applied voltage.
