Correlation of Microstructure and Nanomechanical Properties of Additively Manufactured Inconel 718

被引:0
|
作者
Kim, Allen [1 ]
Vu, Lily [1 ]
Chung, Tony [2 ]
Song, David [2 ]
Wang, Junlan [1 ]
机构
[1] Univ Washington, Dept Mech Engn, Seattle, WA 98195 USA
[2] Blue Origin, Kent, WA 98032 USA
基金
美国国家科学基金会;
关键词
Inconel; 718; electron backscatter diffraction; nanoindentation; property mapping; additive manufacturing; ELASTIC-MODULUS; INDENTATION; HARDNESS; BEHAVIOR;
D O I
10.1115/1.4062776
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Additive manufacturing (AM) has emerged as a crucial technology in recent decades, particularly within the aerospace industry. However, the thermally cyclic nature of these processes introduces significant variations and defects in microstructure, which can adversely affect final part performance and hinder the widespread adoption of the technology. Traditionally, characterization of AM parts has relied on conventional bulk testing methods, which involve analyzing many samples to gather sufficient data for statistical analysis. Unfortunately, these methods are unable to account for local nanoscale variations in material properties caused by the microstructure, as they measure a single averaged property for each tested sample. In this work, we use AM Inconel 718 as a model system in developing a novel approach to correlate nanomechanical properties obtained through nanoindentation with microstructure obtained through electron backscatter diffraction (EBSD). By associating mechanical properties obtained from each indent with the corresponding crystallographic direction, we calculate the weighted average hardness and modulus for each orientation. This enables us to generate inverse pole figure maps depicting the relationship between mechanical properties and crystallographic direction. Our method yields results in good agreement with literature when calculating the part modulus and hardness, while effectively capturing nanoscale variations in properties across the microstructure. The key advantage of this methodology is its capability to rapidly test a single AM part and generate a large dataset for statistical analysis. Complementing existing macroscale characterization techniques, this method can help improve AM part performance prediction and contribute to the wider adoption of AM technologies in the future.
引用
收藏
页数:13
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