Thermal-mechanical coupling predication method for thickness of white layer on surface of hard tuned workpiece

Abstract

The invention belongs to the technical field of machining and relates to a thermal-mechanical coupling predication method for the thickness of a white layer on the surface of a hard tuned workpiece. The method includes the following steps that finite element modeling and simulating are performed in the hard tuning process; stress and strain energy data of the machined surface are extracted from a finite element post analysis result, and critical austenite phase change temperature under influences of coupling of stress, strain and alloying elements is calculated; temperature distribution data underneath the machined surface are extracted from the finite element analysis result, and the thickness of the white layer on the surface is predicated according to temperature distribution and the actual critical phase change temperature. A finite element predication model of the thickness of the white layer on the dry hard tuned surface based on the phase change mechanism is provided and not only can predicate the thickness of the phase change white layer on the surface more accurately under the condition that thermal-mechanical coupling factors are considered, but also discloses the internal mechanism of forming the white layer.

Description

technical field [0001] The invention belongs to the technical field of machining, and relates to a method for predicting the thickness of a white layer on the surface of a hard-cut workpiece. Background technique [0002] In recent years, with the rapid development of various advanced manufacturing methods, hard cutting processing technology has been more and more widely used. Surface integrity, especially the formation of material-degenerated layers on machined surfaces, is one of the greatest concerns in the field of hard cutting technology. Since the surface degenerated layer often appears white under the light microscope, it is usually called "white layer". The microstructure and thickness of the white layer have an important influence on the internal residual stress distribution, friction performance, fatigue resistance and service life of the workpiece. Accurate quantitative prediction of the thickness of the white layer can select the best combination of cutting par...

AI Technical Summary

Problems solved by technology

Regression analysis and artificial intelligence methods require a large amount of experimental data as input parameters, and can only be applied to the range of cutting amount used in the experiment. In the case of high-speed cutting, the change of the formation mechanism of the white layer will cause a large prediction error

Although the prediction model based on finite element analysis is reasonable in terms of the prediction method, if the hardness is used as the critical condition, the critical hardness of the white layer is different under the influence of different formation mechanisms. Therefore, the hardness criterion It cannot reflect the internal formation mechanism of the white layer, and cannot realize the coupling of thermal-mechanical influencing factors

If temperature is used as the critical condition, as the cutting speed increases, thermodynamic coupling factors such as alloying elements in steel, large stress and strain during material deformation, and severe temperature rise caused by tool wear will have a significant impact on the critical phase transition temperature. effect, in which case the white layer thickness cannot be accurately predicted using the nominal critical phase transition temperature

It can be seen that the universality of these prediction models is poor at present, and it cannot achieve high-precision prediction of white layer thickness under different cutting conditions in the cutting process.

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