Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be f...Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.展开更多
In high speed milling aeronautical part,tool condition monitoring(TCM)is very important,because it is prone to get a chatter owing to the low stiffness of thin-walled structures.And the TCM is key technology for autom...In high speed milling aeronautical part,tool condition monitoring(TCM)is very important,because it is prone to get a chatter owing to the low stiffness of thin-walled structures.And the TCM is key technology for automated machining.In this paper,aiming to chatter monitoring in thin-walled structure milling,a variational mode decomposition–energy distribution(VMD-ED)method is proposed to improve the identification accuracy.And a moving average root mean square–mean value(MARMS-MV)identification method and a variational mode decomposition–energy entropy(VMD-EE)identification method are also tested.Identification accuracy and computing time of the three methods are compared.The vibration signals collected from the spindle and worktable are also contrasted.The conducted experimental study shows that,the proposed VMD-ED method offers an identification method for chatter monitoring with greater sensitivity,better stability and less computing time,and mounting the vibration sensor on worktable is better than spindle for a chatter monitoring system.展开更多
由于国内门窗窗型加工设备大多处于半自动化状态,更多依赖于国外进口设备,且存在着加工效率不高、精度低等缺陷,因此提出了门窗材双端刨铣自动换刀加工中心的设计。通过对门窗材双端端头铣型加工工艺分析,完成门窗材双端刨铣自动换刀加...由于国内门窗窗型加工设备大多处于半自动化状态,更多依赖于国外进口设备,且存在着加工效率不高、精度低等缺陷,因此提出了门窗材双端刨铣自动换刀加工中心的设计。通过对门窗材双端端头铣型加工工艺分析,完成门窗材双端刨铣自动换刀加工中心结构总体布局,利用Solidworks软件对加工中心端头铣削结构进行实体建模,确定了加工中心端头铣削结构主机总体结构,并对齐头锯、端头粗铣铣削主轴的切削力和切削功率进行设计计算,得到主锯电机功率7.5 k W、铣削主轴铣削电机功率为7.5 k W,符合加工功率要求,并完成齐头锯组件、粗铣主轴组件、精铣主轴组件、榫头铣座送料结构、刀具位置调整机构、主机机架等结构的设计以及外购件的选型,最后运用ANSYS对粗铣铣削主轴进行静力学分析,得到主轴的应力、应变和变形云图,其最大应力值为10.305 MPa、最大应变值为0.051 527 mm/m、最大变形量为0.010 946 mm,且最大强度、刚度以及变形均在安全范围内,验证了铣削主轴满足设计要求。设计的自动换刀加工中心结构简单,能一次完成门窗材两边齐头、端头铣型工序,自动化程度高,效率高。展开更多
In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same i...In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.展开更多
基金The authors are grateful for the support by National Key Research and Development Program of China(2021YFF0500300,2020YFB1708300)the National Natural Science Foundation of China(52205280,12172041).
文摘Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.
基金co-supported by the National Key Research and Development Program of China(No.2019YFB1704800).
文摘In high speed milling aeronautical part,tool condition monitoring(TCM)is very important,because it is prone to get a chatter owing to the low stiffness of thin-walled structures.And the TCM is key technology for automated machining.In this paper,aiming to chatter monitoring in thin-walled structure milling,a variational mode decomposition–energy distribution(VMD-ED)method is proposed to improve the identification accuracy.And a moving average root mean square–mean value(MARMS-MV)identification method and a variational mode decomposition–energy entropy(VMD-EE)identification method are also tested.Identification accuracy and computing time of the three methods are compared.The vibration signals collected from the spindle and worktable are also contrasted.The conducted experimental study shows that,the proposed VMD-ED method offers an identification method for chatter monitoring with greater sensitivity,better stability and less computing time,and mounting the vibration sensor on worktable is better than spindle for a chatter monitoring system.
文摘由于国内门窗窗型加工设备大多处于半自动化状态,更多依赖于国外进口设备,且存在着加工效率不高、精度低等缺陷,因此提出了门窗材双端刨铣自动换刀加工中心的设计。通过对门窗材双端端头铣型加工工艺分析,完成门窗材双端刨铣自动换刀加工中心结构总体布局,利用Solidworks软件对加工中心端头铣削结构进行实体建模,确定了加工中心端头铣削结构主机总体结构,并对齐头锯、端头粗铣铣削主轴的切削力和切削功率进行设计计算,得到主锯电机功率7.5 k W、铣削主轴铣削电机功率为7.5 k W,符合加工功率要求,并完成齐头锯组件、粗铣主轴组件、精铣主轴组件、榫头铣座送料结构、刀具位置调整机构、主机机架等结构的设计以及外购件的选型,最后运用ANSYS对粗铣铣削主轴进行静力学分析,得到主轴的应力、应变和变形云图,其最大应力值为10.305 MPa、最大应变值为0.051 527 mm/m、最大变形量为0.010 946 mm,且最大强度、刚度以及变形均在安全范围内,验证了铣削主轴满足设计要求。设计的自动换刀加工中心结构简单,能一次完成门窗材两边齐头、端头铣型工序,自动化程度高,效率高。
基金the Science and Technology Major Project of China(No.2019ZX04020001-004,2017ZX04007001)。
文摘In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.
