Additive manufacturing of metals and alloys to achieve heterogeneous microstructures for exceptional mechanical properties
Bibliographic record
Abstract
Metals and alloys with heterogeneous microstructures exhibit an enhanced combination of strength and ductility, compared to their counterparts with homogeneous microstructures.Additive manufacturing (AM) techniques offers a new opportunity to induce heterogeneous microstructures in a wide range of metals and alloys, thereby optimizing their mechanical properties.In this review article, we focus on four types of heterogeneous microstructures induced by AM: lamellar, gradient, laminated, and harmonic ones.We will introduce (i) the tailored generation of heterogeneous microstructures, (ii) the effects of characteristic solidification conditions, and (iii) the intricate phase transformation in laser-based AM process.Furthermore, we discuss the features, advantages, and potential applications of the metals and alloys with heterogeneous microstructures made by AM.To conclude, we discuss current challenges and future opportunities in this field. IMPACT STATEMENTAdditive manufacturing offers the opportunity of creating heterogeneous microstructures that enhance the strength-ductility synergy in metals and alloys.We review generation methods, solidification effects, heat treatment, and potential applications.
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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 | 0.000 |
| 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 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".