Advancements and applications of multiple wire processes in additive manufacturing: a comprehensive systematic review
Bibliographic record
Abstract
The dynamic landscape of additive manufacturing (AM) is undergoing a transformative phase with the advent of multiple wire arc AM (MWAAM) processes. This systematic review offers an exhaustive exploration of the latest advancements and multifaceted applications of these innovative techniques within the realms of AM and welding. Prominently discussed processes encompass Bi-Metallic Wire Arc Additive Manufacturing, Twin Wire Arc Additive Manufacturing, Tandem Gas Metal Arc Welding, Twin-Wire Plasma Arc Additive, and Hybrid Wire Arc Additive Manufacturing. These techniques, instrumental in fabricating an array of materials from titanium aluminides to low-carbon steel, underscore the versatility and potential of modern AM. The application breadth spans key industries such as aerospace, naval, automotive, and energy, highlighting the ubiquity and relevance of these processes. While they promise enhanced productivity, improved material attributes, and economic efficiencies, challenges persist, including the need for meticulous parameter control, an in-depth grasp of foundational physics, and the development of sophisticated predictive models. Projecting into the future of AM, this review anticipates a harmonised integration of computational advancements with automation, positioning these MWAAM processes as pivotal in the next wave of manufacturing innovations.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".