材料科学
纳米压痕
缩进
涂层
复合材料
有限元法
变形(气象学)
微观结构
正交异性材料
应变硬化指数
弹性模量
硬化(计算)
结构工程
图层(电子)
工程类
作者
Kirsten Bobzin,C. Kalscheuer,M. Carlet,Siegfried Schmauder,Vinzenz Guski,Wolfgang Verestek,Muhammad Tayyab
标识
DOI:10.1016/j.surfcoat.2022.129148
摘要
The finite element method (FEM) nanoindentation simulations of nitride hard coatings are either focused on the coating only or contain an axisymmetric model of the coated compound with isotropic material behavior. Moreover, the material model parameters are optimized at only one particular indentation load. The current work aims to develop a 3D FEM model for studying deformation behavior of a CrAlN PVD coated HS6-5-2C tool steel compound during nanoindentation at varying indentation loads. A machine learning (ML)-based algorithm was used to characterize the coating microstructure. This information allowed to set up a 3D FEM model containing a digital clone of the compound microstructure along with the local orientations of the grains embedded into a larger elastically modelled frame. The material model parameters such as Young's modulus E, yield stress Y, strain hardening coefficient B and strain hardening exponent n for coating, interlayer and substrate were determined by fitting the simulated force-displacement curves to the experimental ones through an iterative optimization approach at four different indentation loads. An overall good agreement between the simulated and experimental force-displacement curves along with physically reasonable material model parameters was achieved. The obtained yield stress of CrAlN coating was comparable to ceramics. Moreover, elastic-plastic deformation in interlayer and elastic deformation in substrate at indentation depth below 10 % of the coating thickness was observed. This necessitates the interlayer and substrate consideration for FEM nanoindentation simulations of PVD coated compounds as in the present study. Therefore, the used modeling approach, based on orthotropic elastic material law for the coating combined with 3D digital clone of the compound microstructure, was successfully implemented and validated for low as well as high indentation loads.
科研通智能强力驱动
Strongly Powered by AbleSci AI