Steel-copper functionally graded material produced by twin-wire and arc additive manufacturing (T-WAAM)
Bibliographic record
Abstract
In this work, a functionally graded material (FGM) part was fabricated by depositing a Cu-based alloy on top of a high strength low alloy (HSLA) steel by twin-wire and arc additive manufacturing (T-WAAM). Copper and steel parts are of interest in many industries since they can combine high thermal/electrical conductivity, wear resistance with excellent mechanical properties. However, mixing copper with steel is difficult due to mismatches in the coefficient of thermal expansion, in the melting temperature, and crystal structure. Moreover, the existence of a miscibility gap during solidification, when the melt is undercooled, causes serious phase separation and segregation during solidification which greatly affects the mechanical properties. Copper and steel control samples and the functionally graded material specimen were fabricated and investigated using optical microscopy, scanning electron microscopy, and high energy synchrotron X-ray diffraction. Retained δ-ferrite was found in a Cu matrix at the interface region due to regions with mixed composition. A smooth gradient of hardness and electric conductivity along the FGM sample height was obtained. An ultimate tensile strength of 690 MPa and an elongation at fracture of 16.6% were measured in the FGM part.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".