Enhanced manufacturing quality and mechanical performance of laser powder bed fused TiC/AZ91D magnesium matrix composites
Bibliographic record
Abstract
The addition of ceramic reinforcements provides a promising approach to achieving high-performance magnesium matrix composites. In this work, AZ91D magnesium alloys and 2 wt.% TiC/AZ91D composites have been manufactured by laser powder bed fusion (LPBF) with variations of laser processing parameters. The effect of TiC reinforcement addition on the laser absorption behaviors, forming quality, microstructure evolution and mechanical properties of the magnesium alloys is investigated. The TiC addition improves the interactions of laser with alloy powder and laser absorption rate of alloy powder, and decreases powder spatter of powder bed. The results show that high relative density of ∼99.4% and good surface roughness of ∼12 µm are obtained for the LPBF-fabricated composites. The TiC addition promotes the precipitation of β-Mg17Al12 in the alloys and the transformation of coarse columnar to fine equiaxed grains, where the grains are refined to ∼3.1 µm. The TiC/AZ91D composites exhibit high microhardness of 114.6 ± 2.5 HV0.2, high tensile strength of ∼345.0 MPa and a uniform elongation ∼4.1%. The improvement of tensile strength for the composites is ascribed to the combination of grain refinement strengthening and Orowan strengthening from β-Mg17Al12 precipitates and Al8Mn5 nanoparticles. In the composites, the unmelted TiC particles can act as an anchor for the network structure of β-Mg17Al12 precipitates, effectively impeding crack propagation and enhancing their performance. This work offers an insight to fabricating high-performance magnesium matrix composites by laser additive manufacturing.
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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 | 0.000 | 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) | 0.001 | 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".