PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 22, 2025Science Advances21 citationsOpen Access

Naturally high fatigue performance of a 3D printing titanium alloy across all stress ratios

View Full Paper
ZQZhan QuZZZhenjun ZhangRLRichard Liu

Key Points

  • This titanium alloy exhibits outstanding fatigue performance across all stress ratios, outperforming other materials.
  • Under varying stress conditions, the Ti-6Al-4V alloy shows significant improvements in fatigue reliability and durability.
  • Using additive manufacturing, the unique microstructure enhances performance, benefiting complex engineering components.
  • The findings suggest a broadened application for 3D printing in producing complex structures with improved fatigue resistance.

Abstract

Three-dimensional printing of structural materials, namely, additive manufacturing (AM), has notable advantages in fabricating structurally complex engineering components. These complex components usually endure comprehensive fatigue examination due to their complex stress distribution with varying stress ratios during service. Therefore, it is important to ensure the fatigue reliability of additive manufactured materials across all stress ratios. We found that the AM microstructure itself in a Ti-6Al-4V alloy successfully synthesizes the tripartite advantages of fine prior β grain boundaries, void-free, and fine α grains, which are respectively sensitive to the low, medium, and high stress ratio regions. Under this synergistic effect, the fatigue performance of the natural AM microstructure across all stress ratios not only outperforms all additive manufactured and forged Ti-6Al-4V alloys, but also surpasses other metallic materials. Our finding highlights the potential advantage of additive manufacturing technology in producing complex components with high fatigue resistance, substantially expanding its application scope.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Qu et al. (2025) studied this question.

synapsesocial.com/papers/68af5418ad7bf08b1eadb3e3https://doi.org/10.1126/sciadv.ady0937
Ask AI
Helpful
Bookmark
Share
View Full Paper