Additive Manufacturing in Off-Site Construction: Review and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Additive manufacturing (AM) is one of the pillars of Industry 4.0 to attain a circular economy. The process involves a layer-by-layer deposition of material from a computer-aided-design (CAD) model to form complex shapes. Fast prototyping and waste minimization are the main benefits of employing such a technique. AM technology is presently revolutionizing various industries such as electronics, biomedical, defense, and aerospace. Such technology can be complemented with standardized frameworks to attract industrial acceptance, such as in the construction industry. Off-site construction has the potential to improve construction efficiency by adopting AM. In this paper, the types of additive manufacturing processes were reviewed, with emphasis on applications in off-site construction. This information was complemented with a discussion on the types and mechanical properties of materials that can be produced using AM techniques, particularly metallic components. Strategies to assess cost and material considerations such as Production line Breakdown Structure (PBS) and Value Stream Mapping are highlighted. In addition, a comprehensive approach that evaluates the entire life cycle of the component was suggested when comparing AM techniques and conventional manufacturing options.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it