Grain disintegration and dynamic recrystallization during impact tests of additively manufactured nickel-based alloy 718

被引:0
作者
Sankar, Anjali [1 ]
Manjaiah, M. [1 ,2 ]
Mccarthy, Thomas [1 ]
Pasco, Jubert [1 ]
Ejera, Stan Kristian [3 ]
Aranas, Clodualdo [1 ,3 ]
机构
[1] Univ New Brunswick, Mech Engn, Fredericton, NB, Canada
[2] Natl Inst Technol Warangal, Mech Engn, Warangal, Telangana, India
[3] Univ Philippines, Dept Min Met & Mat Engn, Quezon City, Philippines
来源
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T | 2024年 / 33卷
基金
加拿大创新基金会; 加拿大自然科学与工程研究理事会;
关键词
Alloy; 718; LPBF; Dynamic mechanical response; Constitutive modelling; SHPB; HIGH-TEMPERATURE DEFORMATION; MECHANICAL-PROPERTIES; SHEAR LOCALIZATION; CONSTITUTIVE MODEL; HEAT-TREATMENTS; PLASTIC-FLOW; STRAIN-RATE; INCONEL; BEHAVIOR; MICROSTRUCTURE;
D O I
10.1016/j.jmrt.2024.10.157
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The high-temperature dynamic mechanical response of Alloy 718 produced via laser-powder bed fusion (LPBF) was investigated through compressive Split-Hopkinson Pressure Bar (SHPB) tests. Simulating the typical service conditions of Alloy 718, the tests were conducted at temperatures ranging from 298 K to 773 K and at strain rates of 1000 s-1 to 1500 s-1. Phenomenological material constitutive models, such as the modified versions of Johnson-Cook and Hensel-Spittel models, were developed based on the SHPB test results. Analysis of micro- structural evolution under impact conditions highlighted that columnar grains with high Schmid factors tend to undergo preferential activation and dislocation pile-up. This process leads to the formation of adiabatic shear bands, grain disintegration, and intense lattice rotation, particularly at higher strain rates. Furthermore, increasing the dynamic deformation temperature facilitates the activation of discontinuous dynamic recrystallization (DRX), with strain accumulation promoting localized grain nucleation along heavily dislocated dendritic boundaries. Recognizing the limitations of phenomenological material constitutive models in accurately representing the underlying microstructural evolution, an artificial neural network (ANN)-based constitutive model employing a three-layer backpropagation learning algorithm was implemented, reducing the Average Absolute Relative Error (AARE) to 0.17%.
引用
收藏
页码:4844 / 4857
页数:14
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