Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning

Titanium alloys fabricated by laser powder bed fusion (LPBF) often suffer from limited ductility because of the inherent acicular a � martensite embedded in the columnar parent phase grains (prior-fl grains). The post-built heat treatment at a relatively high temperature (⠃1075 K) necessary for decomposing marten -site results in improved ductility at the cost of strength. It, however, remains difficult to achieve balances between strength and ductility in as-printed conditions due to the huge range of possible compositions of printing process variables. Herein, using LPBF-processed Ti-6Al-4V (Ti64) alloy as an example, we demonstrate that machine learning (ML) is capable of accelerating the discovery of the proper sets of pro-cessing parameters resulting in a superior synergy of strength and ductility (i.e., yield strength, Ys0.2 = 1044 & PLUSMN; 10 MPa, uniform elongation, UEL = 10.5 & PLUSMN; 1.2 % and total elongation = 15 & PLUSMN; 1.5 %). Such property improvement is found to be enabled by an unique refined prior-fl grains decorated by confined au-colony precipitates. In particular, the uniform deformation ability of au martensite is improved due to the enhanced microstructure uniformity achieved by weakening variant selection. ML-based processing parameter optimization approach is thus well-positioned to accelerate the qualification of a wide range of L-PBF manufactured alloys beyond Ti-alloys.& COPY; 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Machine learning is capable of accelerating the discovery of proper processing parameters for producing high-quality alloy with good strength and ductility. The optimization of processing parameters can improve the mechanical properties of titanium alloys by enhancing the microstructure.