Large-scale metal additive manufacturing: a holistic review of the state of the art and challenges
Bibliographic record
Abstract
Additive Manufacturing (AM) has the potential to completely reshape the manufacturing space by removing the geometrical constraints of commercial manufacturing and reducing component lead time, especially for large-scale parts. Coupling robotic systems with direct energy deposition (DED) additive manufacturing techniques allow for support-free printing of parts where part sizes are scalable from sub-metre to multi-metre sizes. This paper offers a holistic review of large-scale robotic additive manufacturing, beginning with an introduction to AM, followed by different DED techniques, the compatible materials and their typical as-built microstructures. Next, the multitude of robotic build platforms that extend the deposition from the standard 2.5 degrees of freedom (DOF) to 6 and 8 DOF is discussed. With this context, the decomposition and slicing of the computerized model will be described, and the challenges of planning the deposition trajectory will be discussed. The different modalities to monitor and control the deposition in an attempt to meet the geometrical and performance specifications are outlined and discussed. A wide range of metals and alloys have been reported and evaluated for large-scale AM parts. These include steels, Ti, Al, Mg, Cu, Ni, Co–Cr and W alloys. Different post-processing steps, including heat treatments, are discussed, along with their microstructures. This paper finally addresses the authors' perspective on the future of the field and the largest knowledge gaps that need to be filled before the commercial implementation of robotic AM.
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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 | 0.002 | 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".