以机器人用RV减速器零部件为研究对象,讨论了其参数化设计系统的设计思路与开发流程,在Pro/E 4.0环境下,利用Pro/Toolkit二次开发工具包和Microsoft Visual Studio 2005作为二次开发工具,成功开发出了RV减速器零部件三维模型的参数化设...以机器人用RV减速器零部件为研究对象,讨论了其参数化设计系统的设计思路与开发流程,在Pro/E 4.0环境下,利用Pro/Toolkit二次开发工具包和Microsoft Visual Studio 2005作为二次开发工具,成功开发出了RV减速器零部件三维模型的参数化设计系统,提高了RV减速器的系列化设计效率,增强了减速器产品设计的灵活性,为减速器参数化通用设计平台的实现奠定了基础.展开更多
The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the bindin...The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the binding energies of 9035 nuclei,the KRR method achieves a root-mean-square deviation of 0.96 MeV,and the KRRoe method remarkably reduces the deviation to 0.17 MeV.By investigating the shell effects,one-nucleon and twonucleon separation energies,odd-even mass differences,and empirical proton-neutron interactions extracted from the learned binding energies,the ability of the machine learning tool to grasp the known physics is discussed.It is found that the shell effects,evolutions of nucleon separation energies,and empirical proton-neutron interactions are well reproduced by both the KRR and KRRoe methods,although the odd-even mass differences can only be reproduced by the KRRoe method.展开更多
文摘以机器人用RV减速器零部件为研究对象,讨论了其参数化设计系统的设计思路与开发流程,在Pro/E 4.0环境下,利用Pro/Toolkit二次开发工具包和Microsoft Visual Studio 2005作为二次开发工具,成功开发出了RV减速器零部件三维模型的参数化设计系统,提高了RV减速器的系列化设计效率,增强了减速器产品设计的灵活性,为减速器参数化通用设计平台的实现奠定了基础.
基金Supported by the National Natural Science Foundation of China(11875075,11935003,11975031,12141501,12070131001)the China Postdoctoral Science Foundation under(2021M700256)+1 种基金the State Key Laboratory of Nuclear Physics and Technology,Peking University(NPT2023ZX01,NPT2023KFY02)the President’s Undergraduate Research Fellowship(PURF)of Peking University
文摘The kernel ridge regression(KRR)method and its extension with odd-even effects(KRRoe)are used to learn the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory.With respect to the binding energies of 9035 nuclei,the KRR method achieves a root-mean-square deviation of 0.96 MeV,and the KRRoe method remarkably reduces the deviation to 0.17 MeV.By investigating the shell effects,one-nucleon and twonucleon separation energies,odd-even mass differences,and empirical proton-neutron interactions extracted from the learned binding energies,the ability of the machine learning tool to grasp the known physics is discussed.It is found that the shell effects,evolutions of nucleon separation energies,and empirical proton-neutron interactions are well reproduced by both the KRR and KRRoe methods,although the odd-even mass differences can only be reproduced by the KRRoe method.
