A brief review on industrial remanufacturing of structural and functional components: Wire-Arc Additive Manufacturing Technique
Bibliographic record
Abstract
Many industries rely heavily on the availability and reliability of complex structural and functional components to execute operations efficiently. However, failure of components during service occurs due to exposure to unfavourable operating conditions, causing wear and tear of these components. The damage will result in costly downtime and potential safety hazards. Repairing, remanufacturing and refurbishing these complex parts is critical. Restoring broken structures into operation ensures the smooth operation of the industry, thus preventing losses. Conventionally, repairing parts poses a challenge. However, Wire-arc additive manufacturing (WAAM), which employs the welding principle, has revolutionised component repairing or remanufacturing. This paper reviews the literature on manufacturing complex parts, repair, remanufacture and refurbishment of broken structural and functional parts using WAAM technology. This paper also highlights the various strategies and techniques currently used to improve the quality of WAAM 3D printed parts. The study further covers the immense potential of WAAM in revolutionising the remanufacturing and repair of components. The review study has provided a roadmap for future research and development to take full advantage of this new cutting-edge manufacturing technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".