Additive Manufacturing of Negative Thermal Expansion Metamaterials Using Steels
Bibliographic record
Abstract
Abstract Negative thermal expansion (NTE) materials are critical for applications sensitive to thermal expansion, such as precision instrumentation and aerospace systems, but their use is often limited by reliance on rare or expensive materials. Architected NTE metamaterials fabricated from ubiquitous structural alloys like steel present a transformative and scalable alternative. This study focuses on fabricating such metamaterials using laser powder bed fusion (LPBF) of AISI 304L stainless steel and SAE grade 300 maraging steel in bi‐material configurations. Initial efforts optimize LPBF parameters to optimize interfacial strength through detailed process‐structure‐property investigations. Mechanical properties across the interface are characterized using uniaxial tensile testing, nanoindentation, and scratch resistance measurements, while scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDS), and electron backscatter diffraction (EBSD) are utilized to analyze interfacial microstructure and bonding. Multiple support‐free, thermally responsive bi‐material lattice topologies are computationally designed to ensure manufacturability using LPBF, with their selection informed by finite element simulations. Subsequently, lattices are fabricated using optimized LPBF parameters and experimentally evaluated for their thermal expansion performance using digital image correlation (DIC). All lattice variants demonstrate NTE behaviour, with octagonal and dodecagonal bipyramid configurations achieving the highest magnitudes of negative thermal expansion coefficient.
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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 | 0.001 |
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".