Titanium alloy has been applied in the field of aerospace manufacturing for its high specific strength and hardness.Nonetheless,these properties also cause general problems in the machining,such as processing ineffici...Titanium alloy has been applied in the field of aerospace manufacturing for its high specific strength and hardness.Nonetheless,these properties also cause general problems in the machining,such as processing inefficiency,serious wear,poor workpiece face quality,etc.Aiming at the above problems,this paper carried out a comparative experimental study on titanium alloy milling based on the CAMCand BEMC.The variation law of cutting force and wear morphology of the two tools were obtained,and the wear mechanism and the effect of wear on machining quality were analyzed.The conclusion is that in contrast with BEMC,under the action of cutting thickness thinning mechanism,the force of CAMC was less,and its fluctuation was more stable.The flank wear was uniform and near the cutting edge,and the wear rate was slower.In the early period,the wear mechanism of CAMC was mainly adhesion.Gradually,oxidative wear also occurred with milling.Furthermore,the surface residual height of CAMC was lower.There is no obvious peak and trough accompanied by fewer surface defects.展开更多
Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performan...Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51975168).
文摘Titanium alloy has been applied in the field of aerospace manufacturing for its high specific strength and hardness.Nonetheless,these properties also cause general problems in the machining,such as processing inefficiency,serious wear,poor workpiece face quality,etc.Aiming at the above problems,this paper carried out a comparative experimental study on titanium alloy milling based on the CAMCand BEMC.The variation law of cutting force and wear morphology of the two tools were obtained,and the wear mechanism and the effect of wear on machining quality were analyzed.The conclusion is that in contrast with BEMC,under the action of cutting thickness thinning mechanism,the force of CAMC was less,and its fluctuation was more stable.The flank wear was uniform and near the cutting edge,and the wear rate was slower.In the early period,the wear mechanism of CAMC was mainly adhesion.Gradually,oxidative wear also occurred with milling.Furthermore,the surface residual height of CAMC was lower.There is no obvious peak and trough accompanied by fewer surface defects.
基金supported by the National Natural Science Foundation of China(61871420)the Natural Science Foundation of Sichuan Province,China(23NSFSC2916)the introduction of talent,Southwest MinZu University,China,funding research projects start(RQD2021064).
文摘Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.
