A Review on Extrusion Additive Manufacturing of Pure Copper
Bibliographic record
Abstract
Copper, due to its high thermal and electrical conductivity, is used extensively in many industries such as electronics, aerospace, etc. In the literature, researchers have utilized different additive manufacturing (AM) techniques to fabricate parts with pure copper; however, each technique comes with unique pros and cons. Among others, material extrusion (MEX) is a noteworthy AM technique that offers huge potential to modify the system to be able to print copper parts without a size restriction. For that purpose, copper is mixed with a binder system, which is heated in a melt chamber and then extruded out of a nozzle to deposit the material on a bed. The printed part, known as the green part, then goes through the de-binding and sintering processes to remove all the binding materials and densify the metal parts, respectively. The properties of the final sintered part depend on the processing and post-processing parameters. In this work, nine published articles are identified that focus on the 3D printing of pure copper parts using the MEX AM technique. Depending on the type of feedstock and the feeding mechanism, the MEX AM techniques for pure copper can be broadly categorized into three types: pellet-fed screw-based printing, filament-fed printing, and direct-ink write-based printing. The basic principles of these printing methods, corresponding process parameters, and the required materials and feedstock are discussed in this paper. Later, the physical, electrical, and mechanical properties of the final parts printed from these methods are discussed. Finally, some prospects and challenges related to the shrinkage of the printed copper part during post-processing are also outlined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".