High Cycle Fatigue and Very High Cycle Fatigue Performance of Selective Laser Melting Ti-6Al-4V Titanium Alloy—A Review
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) of metallic alloys, especially titanium (Ti), has recently received considerable attention because of its significant role in designing and developing many structural components with complex geometries in aerospace, defense, and biomechanical industries. AM technology based on selective laser melting (SLM) allows the production of lightweight structures with geometric flexibility, which has not been otherwise possible by the conventional manufacturing process. SLM-fabricated Ti-6Al-4V components often experience long loading histories in high cycle fatigue (HCF) and even very high cycle fatigue (VHCF) regimes. As a result, it is paramount to systematically investigate those components’ fatigue behavior under both HCF and VHCF conditions. However, HCF and VHCF performances of SLM-Ti-6Al-4V alloy are still not fully understood because of the complex nature of fatigue responses in those regimes resulting from the defects/porosity and number of process parameters. In this context, the successful application of load-bearing components in both HCF and VHCF regimes necessitates optimizing process parameters and post-treatments for the optimal fatigue performance point of view. Several recent studies dealing with Ti-6Al-4V parts manufactured by SLM have explored parameters affecting fatigue performance in HCF and VHCF regimes. This article presents a systematic and critical review analysis of recent findings related to critical parameters, particularly residual stress, surface roughness, build parameters, build orientation microstructural features, post-process treatment, manufacturing deficiencies, specimen geometries, load ratio affecting mechanical and fatigue properties, especially in HCF and VHCF regimes. The current study also aims to identify several crucial topics that need to be addressed for SLM Ti-6Al-4V alloy to effectively utilize its full potential in the designing of advanced structural components.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".