Selective Laser Melting of Aluminum and Titanium Matrix Composites: Recent Progress and Potential Applications in the Aerospace Industry
Bibliographic record
Abstract
Selective laser melting (SLM) is a near-net-shape time- and cost-effective manufacturing technique, which can create strong and efficient components with potential applications in the aerospace industry. To meet the requirements of the growing aerospace industrial demands, lighter materials with enhanced mechanical properties are of the utmost need. Metal matrix composites (MMCs) are extraordinary engineering materials with tailorable properties, bilaterally benefiting from the desired properties of reinforcement and matrix constituents. Among a wide range of MMCs currently available, aluminum matrix composites (AMCs) and titanium matrix composites (TMCs) are highly potential candidates for aerospace applications owing to their outstanding strength-to-weight ratio. However, the feasibility of SLM-fabricated composites utilization in aerospace applications is still challenging. This review addresses the SLM of AMCs/TMCs by considering the processability (densification level) and microstructural evolutions as the most significant factors determining the mechanical properties of the final part. The mechanical properties of fabricated MMCs are assessed in terms of hardness, tensile/compressive strength, ductility, and wear resistance, and are compared to their monolithic states. The knowledge gained from process–microstructure–mechanical properties relationship investigations can pave the way to make the existing materials better and invent new materials compatible with growing aerospace industrial demands.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".