Laser Powder Bed Fusion of Low and Negative Thermal Expansion Metamaterials
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) is a metal additive manufacturing (AM) technique that creates objects layer by layer from a bed of loose powder, using a laser beam as the heat source. This layer-wise approach allows for the fabrication of highly complex structures and intricate geometries with high accuracy, including solid, porous, and lattice structures. LPBF offers significant potential for use in industries such as aerospace, biomedical, and automotive due to its ability to fabricate unique and sophisticated designs. This technology has recently attracted significant attention for the fabrication of multimaterial parts with improved properties and applicability in different fields. However, challenges persist in understanding the relationship between process parameters and the properties of resulting multimaterial parts and interfaces. Additionally, limitations exist in design and interface selection for multimaterial fabrication using this technique. Negative thermal expansion (NTE) metamaterials, discussed in this research, are mechanical structures that show negative expansion properties by contracting with increase in temperature, while expanding with a decrease in temperature. These metamaterials are typically multimaterial systems where constituents with positive coefficients of thermal expansion (CTE) are strategically integrated, resulting in an overall NTE effect in one or more directions This research focuses on the design, simulation, and fabrication of negative thermal expansion (NTE) metamaterials using Laser Powder Bed Fusion (LPBF) with Grade 304L Stainless Steel (SS304L), Grade 300 Maraging Steel (MS300), and Invar 36 (Invar) alloys. Bimaterial combinations of SS304L-MS300 and SS304L-Invar were explored. After determining the optimal processing parameters, results showed that a robust, defect-free interface could be achieved in both combinations. Various lattice structures were designed based on these alloy pairs and analyzed using finite element analysis. The designs with the high NTE potential were successfully fabricated through LPBF, using optimal interface parameters. Thermal expansion testing of the fabricated structures demonstrated NTE behavior in line with FEA analysis predictions.
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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) | 0.000 | 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".