Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
Bibliographic record
Abstract
Additive Manufacturing (AM) is an advanced technology that has been primarily driven by the demand for production efficiency, minimized energy consumption, and reduced carbon footprints. This process involves layer-by-layer material deposition based on a Computer-Aided Design (CAD) model. Compared to traditional manufacturing methods, AM has enabled the development of complex and topologically functional geometries for various service parts in record time. However, there are limitations to mass production, the building rate, the build size, and the surface quality when using metal additive manufacturing. To overcome these limitations, the combination of additive manufacturing with traditional techniques such as milling and casting holds the potential to provide novel manufacturing solutions, enabling mass production, improved geometrical features, enhanced accuracy, and damage repair through net-shape construction. This amalgamation is commonly referred to as hybrid manufacturing or multi-material additive manufacturing. This review paper aimed to explore the processes and complexities in hybrid materials, joining techniques, with a focus on maraging steels. The discussion is based on existing literature and focuses on three distinct joining methods: direct joining, gradient path joining, and intermediate section joining. Additionally, current challenges for the development of the ideal heat treatment for hybrid metals are discussed, and future prospects of hybrid additive manufacturing are also covered.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".