Research On The Surface Roughness Assessment Methods Of MEDM

Machined surface roughness is an important indicator to measure the surface topography of a work piece.The size of the machined surface roughness directly or indirectly affects the performance of the work piece.The research on the machined surface roughness evaluation of micro Electrical Discharge Machining(EDM for short)work pieces is the basic support to ensure the application of micro EDM technology in the field of micro machines and microsystems.The evaluation methods,measurement systems and estimation models for machined surface roughness of micro EDM work pieces can realize on-line,non-contact,high-precision and high-efficiency determination of work piece surface roughness parameters,and achieve high-precision,complex-shaped micro-devices and microchips,and the purpose of the fine processing tasks.In this paper,the machined surface roughness evaluation of micro EDM work pieces is studied in depth,focusing on the determination of the reference line and datum surface involved in machined surface roughness evaluation,the design of on-line and non-contact measurement systems,and the prediction evaluation of machined surface roughness.The specific work is as followsAiming at the problem of determining the machined surface roughness of the work piece by the two-dimensional reference line,the complete matrix operation is used to complete the end effect mitigation,zero phase characteristics and high computational efficiency data filtering while ensuring the filtering characteristics of the Gaussian filter.The Gaussian filter fast algorithm obtains the Gaussian filter datum surface of the work piece machined surface roughness,which greatly improves the filtering precision,and the calculation amount is relatively small and the calculation speed is fast,which significantly improves the traditional Gaussian filter evaluation method for the machined surface roughness evaluation of micro EDM.Aiming at the requirement of on-line measurement of micro EDM work pieces,the machined surface roughness measurement system based on machine vision was studied.A machined surface roughness measurement system based on CCD camera was designed,and the complete process from image acquisition,image preprocessing and roughness evaluation was completed.Considering the advantages of laser-scattered fiber-optic sensors,a machined surface roughness measurement system based on multi-wavelength fiber sensors was designed to achieve a relatively high-precision measurement of work piece machined surface roughness.Aiming at the roughness evaluation requirements of micro EDM work pieces,the machined surface roughness evaluation algorithm based on artificial neural network is designed considering the surface image and surface topography data acquired by machine vision:the machined surface roughness prediction algorithm based on polynomial network is designed,the neural network is trained combining with the machined surface roughness data obtained by contact measurement method,the roughness prediction model is established to build the relationship between surface texture and machined surface roughness of the work piece;considering that the neuro-fuzzy system combines the advantages of neural network and fuzzy system,the machined surface roughness prediction algorithm based on neuro-fuzzy system is designed to establish the relationship between actual machined surface roughness and surface image texture features.The machined surface roughness can be made prediction evaluation by approximate modeling of machining surface roughness.