Surface roughness monitoring model based on data mining and construction method

Abstract

The invention belongs to the technical field of information retrieval and database structures, and discloses a surface roughness monitoring model based on data mining and a construction method. The surface roughness model is established based on variance analysis and regression analysis, the incidence relation between cutting force and vibration signals and the surface roughness is determined according to the clustering result, and blindness in the cutting signal selection process is greatly reduced. A multi-sensor technology is applied, force and vibration signals in the cutting process are collected in real time, the cutting signals are decomposed and reconstructed on the basis of singular spectrum analysis, interference generated by noise signals can be effectively reduced, and characteristic quantity extraction is facilitated. Time domain and frequency domain results of cutting force and vibration signals are analyzed, feature extraction is carried out through correlation selection, a surface roughness prediction model is established through a radial basis function neural network, prediction precision and the intelligent level can be greatly improved, and online real-time prediction can be achieved.

Description

technical field [0001] The invention belongs to the technical field of information retrieval and database structure, and in particular relates to a data mining-based surface roughness monitoring model and a construction method. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: In metal cutting processing, geometric accuracy and surface quality determine the processing quality, so online acquisition of the processing surface quality has practical significance. The surface quality of the workpiece includes geometric and physical characteristics. Surface roughness is one of its important indicators. It is a reflection of the microscopic appearance of the workpiece surface during the formation process. The research on surface roughness mainly includes theoretical modeling, design experiments, and artificial intelligence. , online acquisition of surface roughness belongs to the artificial intelligence method. At pr...

AI Technical Summary

Problems solved by technology

[0003] In summary, the problems in the prior art are: The causes of surface roughness are complicated and there are many influencing factors

At present, the surface roughness is mostly calculated by empirical formula and finite element simulation, and the level of intelligence is low, and online monitoring cannot be realized

The online monitoring of surface roughness is mainly based on direct monitoring, which is easily affected by the processing site and cannot be widely used in workshops and factories

There is no suitable and effective signal processing method for the cutting physical quantities collected by the indirect monitoring method, and the degree of fusion between the signals is poor, and there is no scientific guidance method for feature selection

Images