Additive Manufacturing of Ti-Based Alloys: Microstructure, Mechanical Properties, and Corrosion Behavior
Bibliographic record
Abstract
Implementation of rapid prototyping additive manufacturing (AM) has revolutionized and accelerated applications of materials in manufacturing aviation components, personalized prosthetics, and implants with complex geometries and microstructures. Due to its highly-versatile material processing and precise structure control, AM has been widely employed in manufacturing titanium (Ti) and its alloys, as seen in AM-based techniques, including fused deposition modeling (FDM), techniques based on powder bed fusion (PBF) including selective laser sintering (SLS), selective laser melting (SLM), and electron beam melting (EBM), and methods based on direct energy deposition (DED) such as laser engineered net shaping (LENS) and wire arc additive manufacturing (WAAM). Processing Ti alloys, however, is a challenging task due to its complications associated with the processing parameters and the influences on structures and properties of manufactured parts or products. In this paper, we review the AM of Ti and its alloys, focusing on the microstructure of AMed Ti-based parts or products and their mechanical and corrosion properties. This study also addresses the potential of AM methods for the production of complicated components, including cellular structures, and their utilization in the aviation and medical fields. Key challenges and trends of the FDM, PBF-based, and DED methods are also identified and discussed, along with recommendations for future studies on AM-fabricated Ti alloys for improved properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".