Advancements in Additive Manufacturing for Copper-Based Alloys and Composites: A Comprehensive Review
Bibliographic record
Abstract
Copper-based materials have long been used for their outstanding thermal and electrical conductivities in various applications, such as heat exchangers, induction heat coils, cooling channels, radiators, and electronic connectors. The development of advanced copper alloys has broadened their utilization to include structural applications in harsh service conditions found in industries like oil and gas, marine, power plants, and water treatment, where good corrosion resistance and a combination of high strength, wear, and fatigue tolerance are critical. These advanced multi-component structures often have complex designs and intricate geometries, requiring extensive metallurgical processing routes and the joining of the individual components into a final structure. Additive manufacturing (AM) has revolutionized the way complex structures are designed and manufactured. It has reduced the processing steps, assemblies, and tooling while also eliminating the need for joining processes. However, the high thermal conductivity of copper and its high reflectivity to near-infrared radiation present challenges in the production of copper alloys using fusion-based AM processes, especially with Yb-fiber laser-based techniques. To overcome these difficulties, various solutions have been proposed, such as the use of high-power, low-wavelength laser sources, preheating the build chamber, employing low thermal conductivity building platforms, and adding alloying elements or composite particles to the feedstock material. This article systematically reviews different aspects of AM processing of common industrial copper alloys and composites, including copper-chrome, copper-nickel, tin-bronze, nickel-aluminum bronze, copper-carbon composites, copper-ceramic composites, and copper-metal composites. It focuses on the state-of-the-art AM techniques employed for processing different copper-based materials and the associated technological and metallurgical challenges, optimized processing variables, the impact of post-printing heat treatments, the resulting microstructural features, physical properties, mechanical performance, and corrosion response of the AM-fabricated parts. Where applicable, a comprehensive comparison of the results with those of their conventionally fabricated counterparts is provided.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".