The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characte...The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characteristics and theoretic analysis between two electrodes are also discussed based on discharge waveform. By using composite cooling liquid which has strong washing ability, the efficiency in the first stable cutting phase has reached more than 200 mm^2/min, and the roughness of the surface has reached Ra〈0.8 μm after the fourth cutting with more than 50 mm^2/min average cutting efficiency. It is pointed out that cutting situation of the wire cut electrical discharge machine with high wire traveling speed （HSWEDM） is better than the wire cut electrical discharge machine with low wire traveling speed （LSWEDM） in the condition of improving the cooling liquid washing ability. The machining indices of HSWEDM will be increased remarkably by using the composite cooling liquid.展开更多
This paper presents an application of the sparse Bayesian learning(SBL)algorithm to linear inverse problems with a high order total variation(HOTV)sparsity prior.For the problem of sparse signal recovery,SBL often pro...This paper presents an application of the sparse Bayesian learning(SBL)algorithm to linear inverse problems with a high order total variation(HOTV)sparsity prior.For the problem of sparse signal recovery,SBL often produces more accurate estimates than maximum a posteriori estimates,including those that useℓ1 regularization.Moreover,rather than a single signal estimate,SBL yields a full posterior density estimate which can be used for uncertainty quantification.However,SBL is only immediately applicable to problems having a direct sparsity prior,or to those that can be formed via synthesis.This paper demonstrates how a problem with an HOTV sparsity prior can be formulated via synthesis,and then utilizes SBL.This expands the class of problems available to Bayesian learning to include,e.g.,inverse problems dealing with the recovery of piecewise smooth functions or signals from data.Numerical examples are provided to demonstrate how this new technique is effectively employed.展开更多
基金Provincial Key Laboratory of Precision and Micro-Manufacturing Technology of Jiangsu,China(No.Z0601-052-02).
文摘The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characteristics and theoretic analysis between two electrodes are also discussed based on discharge waveform. By using composite cooling liquid which has strong washing ability, the efficiency in the first stable cutting phase has reached more than 200 mm^2/min, and the roughness of the surface has reached Ra〈0.8 μm after the fourth cutting with more than 50 mm^2/min average cutting efficiency. It is pointed out that cutting situation of the wire cut electrical discharge machine with high wire traveling speed （HSWEDM） is better than the wire cut electrical discharge machine with low wire traveling speed （LSWEDM） in the condition of improving the cooling liquid washing ability. The machining indices of HSWEDM will be increased remarkably by using the composite cooling liquid.
基金supported in part by NSF-DMS 1502640,NSF-DMS 1912685,AFOSR FA9550-18-1-0316Office of Naval Research MURI grant N00014-20-1-2595.
文摘This paper presents an application of the sparse Bayesian learning(SBL)algorithm to linear inverse problems with a high order total variation(HOTV)sparsity prior.For the problem of sparse signal recovery,SBL often produces more accurate estimates than maximum a posteriori estimates,including those that useℓ1 regularization.Moreover,rather than a single signal estimate,SBL yields a full posterior density estimate which can be used for uncertainty quantification.However,SBL is only immediately applicable to problems having a direct sparsity prior,or to those that can be formed via synthesis.This paper demonstrates how a problem with an HOTV sparsity prior can be formulated via synthesis,and then utilizes SBL.This expands the class of problems available to Bayesian learning to include,e.g.,inverse problems dealing with the recovery of piecewise smooth functions or signals from data.Numerical examples are provided to demonstrate how this new technique is effectively employed.
