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

Abstract

Titanium alloys fabricated by laser powder bed fusion (LPBF) often suffer from limited ductility because of the inherent acicular α′ martensite embedded in the columnar parent phase grains (prior-β grains). The post-built heat treatment at a relatively high temperature (∼1075 K) necessary for decomposing martensite 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 processing parameters resulting in a superior synergy of strength and ductility (i.e., yield strength, Ys0.2 = 1044 ± 10 MPa, uniform elongation, UEL = 10.5 ± 1.2 % and total elongation = 15 ± 1.5 %). Such property improvement is found to be enabled by an unique refined prior-β grains decorated by confined α′-colony precipitates. In particular, the uniform deformation ability of α′ 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.

Keywords

• Machine learning
• Laser powder bed fusion
• Ti-6Al-4V
• Strength–ductility trade-off