| Five-axis CNC system can process complex parts and play a decisive role in modern manufacturing.However,due to the closed structure of the traditional CNC system without its most basic source code,its development and smart upgrade have been greatly limited that new breakthroughs can not be obtained before them.In this paper,the open CNC system is divided into modules structure,the decoding module and fault diagnosis module in open CNC system are mainly studied to lay a solid foundation for the establishment of a complete open CNC system.First of all,it is determined that a compiled decoding mode is adopted after analyzing the function of the decoding module.The selection of the decoding mode determines that the decoding module of the CNC system belongs to a nonreal-time system.Because NC code has modal quantity and non-modal quantity,in order to facilitate the management of G code and M code,it divided codes into groups and proposed the architecture of decoding module.Secondly,the storage structure of the intermediate code of the compiled decoding module is studied.It chooses the double-ended queue storage structure as the storage structure of decoding module after analyzing the characteristics of it.Through the analysis of the error-checking function of the previous numerical control code,the design of the error-detecting function is improved and the development and experiment of the error-correcting function of the code are realized to verify the feasibility of the decoding module.Then,by analyzing the fault diagnosis of machining and motor,the composition and structure of the fault diagnosis module are studied.The fault diagnosis process is put forward from the practical point of engineering and the intelligent fault diagnosis module framework is also listed.This dissertation focuses on the vibration signal in fault diagnosis and real-time acquisition function based on RTX.Specific signals are collected by vibration signal acquisition equipment,and fault simulation and related experimental verification are completed combined with "S" specimen.Finally,after analyzing the wavelet and particle swarm optimization algorithm,the intelligent fault diagnosis method combining the above algorithms is put forward.On the basis of wavelet analysis,the particle swarm optimization is used to optimize the parameters of the wavelet to obtain more accurate information.The feasibility of this algorithm is verified with experiments.The fault macro is used to combine the decoding module and the fault diagnosis module to complete the data flow fusion among the modules. |
