location: Current position: Home >> Scientific Research >> Paper Publications

Modeling of dynamic recrystallization in white layer in dry hard cutting by finite elementcellular automaton method

Hits:

Indexed by:期刊论文

Date of Publication:2018-09-01

Journal:JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY

Included Journals:EI、SCIE

Volume:32

Issue:9

Page Number:4299-4312

ISSN No.:1738-494X

Key Words:Dry hard cutting; Dynamic recrystallization; Finite element model; Cellular automaton model; White layer

Abstract:White layer formed in hard cutting process has great influence on surface quality of the workpiece, simulation of the white layer has great significance. Dynamic recrystallization critical temperature model is derived to calculate the critical temperature of the dynamic recrystallization in the white layer. A finite element model was developed to simulate the hard cutting process based on the Johnson-Cook constitutive equation. The dynamic recrystallization critical temperature was derived based on the true stress-strain curves obtained by the split Hopkinson pressure bar experiments. The cellular automaton model which aims to simulate the white layer grains formed by the dynamic recrystallization process in hard cutting is established. The temperature and strain data extracted from the finite element model are used in the cellular automaton model. The contrast between the simulation and experimental results demonstrates that the cellular automaton model can simulate the dynamic recrystallization process in the white layer accurately. The dynamic recrystallization processes in the white layer under different cutting speed and flank wear are simulated based on the finite element - cellular automaton model. The results show that the dynamic recrystallization grain size of the white layer decreases with the increase in cutting speed and tool wear.

Pre One:Multidisciplinary and Multifidelity Design Optimization of Electric Vehicle Battery Thermal Management System

Next One:Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data