Prediction of Fracture Behavior of 6061 Aluminum Alloy Based on GTN Model

Abstract

To determine the Gurson-Tvergaard-Needleman (GTN)damage model parameters of 6061 aluminum alloy after secondary heat treatment, the uniaxial tensile test was carried out on the aluminum alloy circular arc specimen, and the mechanical properties parameters and the load-displacement curve of aluminum alloy tube were obtained. With the help of the finite element reverse method, scanning electron microscope and a orthogonal test method, the GTN damage model parameters (f0, fN, fC, and fF) were calibrated, and their values were 0.004535, 0.04, 0.1, and 0.2135, respectively. Then the shear specimen and notch specimen were designed to verify the damage model, the results show that the obtained GTN damage model parameters can effectively predict the fracture failure of 6061 aluminum alloy after secondary heat treatment during the tensile process.

The relevance of breast motions and gaits in running exercises

“…The principle for the GA algorithm is to utilize individuals to represent the initial weights and thresholds of the BP neural network. Meanwhile, the GA algorithm takes the prediction error of the initial neural network as an individual fitness value to find the optimal initial parameters (Ding et al, 2020). The parameters of the GA algorithm were initialized firstly in this study.…”

“…The neural network can approximate any continuous function and square productive function with arbitrary accuracy (Zhou & Ma, 2019). Thus, this method has been widely applied to deal with non-linear problems such as girdle pressure prediction (Zhou & Ma, 2020), fabric surface detection (Jin et al, 2020), and flow stress prediction (Ding et al, 2020). However, the BP neural network has great defects in threshold and weight initialization.…”

“…However, the BP neural network has great defects in threshold and weight initialization. To avoid the BP neural network falling into the local optimum, researchers (Ding et al, 2020;Zhang & Guo, 2019;Zhou & Ma, 2020) usually adopted the genetic algorithm (GA) and the particle swarm optimization (PSO) to optimize the BP neural network. The GA is a computational model of biological evolution.…”

“…This method searches for the optimal solution by simulating the natural evolutionary process (Zhang & Guo, 2019). The PSO is a kind of optimization algorithm based on iteration, and it can search the optimal threshold and weight of the BP neural network in a large space (Ding et al, 2020).…”

Failure behaviors and processing maps with failure domains for hot compression of a powder metallurgy Ni-based superalloy

“…Zhao et al [15] used the GTN damage model to simulate the influence of MC carbides on fracture behaviors of an Inconel 718 sheet during thermally assisted deformation. The GTN model has been also widely applied or extended for modeling the ductile fracture of other metals such as steel [16,17], Al [18][19][20], Mg [21], and under a broad range of stress states [22][23][24]. More about the GTN model and its modifications can be referred to from a recent review by Lee and Basaran [25].…”