Microstructure, Mechanical Properties, and Constitutive Models for Ti–6Al–4V Alloy Fabricated by Selective Laser Melting (SLM)

The mechanical performances and microstructure of Ti–6Al–4V built by selective laser melting were evaluated by optical microscopy, transmission electron microscopy, and room temperature tensile testing, and compared with the wrought and as-cast material. The flow behavior of the...

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Bibliographic Details
Main Authors: Pan Tao, Jiangwei Zhong, Huaixue Li, Quandong Hu, Shuili Gong, Qingyan Xu
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Metals
Subjects:
Online Access:https://www.mdpi.com/2075-4701/9/4/447
Description
Summary:The mechanical performances and microstructure of Ti&#8211;6Al&#8211;4V built by selective laser melting were evaluated by optical microscopy, transmission electron microscopy, and room temperature tensile testing, and compared with the wrought and as-cast material. The flow behavior of the as-produced Ti&#8211;6Al&#8211;4V at temperatures varying from 700&#8211;900 &#176;C at an interval of 50 &#176;C and strain rates ranging from 10<sup>&#8722;2</sup>&#8211;10<sup>1</sup> s<sup>&#8722;1</sup> was experimentally acquired. According to the experimental measurement, the Johnson&#8211;Cook, modified Arrhenius model, and artificial neural network were constructed. A comparative investigation on the predictability of established models was performed. The as-produced microstructure is made up of non-equilibrium martensite and columnar grains, leading to higher strength and lower ductility with respect to the conventional material. In room temperature tensile tests, the SLMed Ti&#8211;6Al&#8211;4V shows the characteristics of continuous yielding and unobvious work-hardening. The flow stress rapidly reaches the peak, and the softening rate depends on the strain rates and deformed temperatures in hot compression. The Johnson&#8211;Cook model could well predict the flow stress during quasi-static tensile deformation, but the model constants might vary with the process conditions. For dynamic compression, the artificial neural network exhibits higher accuracy to fit the flow stress of SLMed Ti&#8211;6Al&#8211;4V, and higher error to predict the conditions out of the model data, compared to the modified Arrhenius model involving the compensation of strain rate and strain.
ISSN:2075-4701