Review of Tribological and Wear Behavior of Alloys Fabricated via Directed Energy Deposition Additive Manufacturing
Bibliographic record
Abstract
Additive manufacturing (AM) is a rapidly evolving technology that enables the fabrication of complex 3D components across a wide range of materials and applications. Among various AM techniques, direct energy deposition (DED) has gained significant attention for its ability to produce metal and alloy components with moderate geometric complexity while maintaining a high deposition rate. This makes DED particularly suitable for real-world applications, including in-situ repair and restoration of metallic parts. Due to the nature of the DED process, components undergo extreme heating and cooling cycles, leading to microstructural evolution, process-induced defects, and variations in properties. While extensive research has explored the microstructure and mechanical properties of DED-fabricated alloys, studies on their surface degradation remain incomplete. Corrosion behavior has been well documented, given its significance in AM alloys; however, their tribological performance remains largely unexplored. This paper provides a comprehensive review of the wear behavior of DED-manufactured alloys, emphasizing the potential of DED technology for producing durable components. Specifically, it examines the wear characteristics of four key material groups—Fe-based, Ni-based, Ti-based, and Cu-based alloys—by summarizing existing studies and analyzing the underlying mechanisms influencing their wear resistance. Finally, the paper identifies research gaps and outlines future directions to advance the understanding of wear performance in DED alloys, paving the way for further innovation in this field.
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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) | 0.001 | 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".