Tailoring the microstructure, physical, and mechanical properties of pure copper using various additive manufacturing techniques
Bibliographic record
Abstract
This study focuses on the utilization of additive manufacturing (AM) for enhancing the production of essential copper-based components, due to their exceptional thermal and electrical conductivity attributes. In this context, an original copper-tellurium cast electrode was completely evaluated and compared with AM pure Cu electrodes, which were produced using laser powder bed fusion (LPBF), LPBF followed by hot isostatic pressing (LPBF-HIP), and binder jetting techniques. The investigation demonstrated that pure copper electrodes produced by LPBF-HIP process showed more homogeneous microstructure with the least porosity (0.14 ± 0.05 %). The uniaxial tensile test results demonstrated that the sample produced via the LPBF-HIP process exhibited superior toughness, with a yield strength of 205 MPa and an ultimate tensile strength of 285 MPa. Furthermore, elongation increased significantly, reaching 54 %. Furthermore, the lowest ultimate tensile strength, measured at 223 MPa, was estimated for the binder jet sample (Markforged), while the lowest elongation, at 34 %, was recorded for the LPBF sample. Although the binder jet samples exhibited a smaller average grain size compared to those produced by LPBF and HIP, it has been determined that porosity plays a more significant role in influencing the mechanical and physical properties of this metal. The physical properties, including thermal conductivity and electrical conductivity, of the samples produced via AM showed that the samples produced using HIP exhibited values closest to the reference cast sample, with measurements of 374 W/m·K and 5.72 × 10 7 S/m, respectively. • Various additive manufacturing techniques were employed to produce pure copper components. • The microstructure, physical and mechanical properties of the samples changed with variations in production techniques. • The influence of microstructure on physical and mechanical properties was assessed. • The effect of HIP treatment on removing the porosities and modifying the as-built LPBF samples was studied. • HIPed pure copper sample exhibited a more uniform microstructure, and better properties compared to the other additively manufactured samples.
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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 | 0.001 |
| 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 | 0.001 | 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".