Transitioning additive manufacturing from rapid prototyping to high‐volume production: A case study of complex final products
Bibliographic record
Abstract
Abstract This paper seeks answers to the question: what are the key factors that enable the scaling of additive manufacturing (AM) from rapid prototyping to high‐volume production? Using a longitudinal case study, we collected primary and secondary data to trace the AM scaling journey of AeroCo, a highly innovative aerospace firm. Based on the case findings, we position AM as a whole system technology because it can print components for a wide range of subsystems in a complex final product. Scaling AM requires a significant realignment of existing, and often deeply entrenched, new technology, and product development processes. To achieve this alignment, AeroCo formed institutional alliances with the UK government and universities to establish university technology centers, which facilitated early stage ideation and “catapult” centers, which enabled high‐volume testing in factory‐like facilities. The case reveals how multiple functions needed to integrate, including research and development, product design, and future programs, to ensure that design changes cascaded from one subsystem to another, and that new technologies were linked to a future product to create a final product pull. These findings inform a managerial framework for additive manufacturing scaling that is generalizable to other digital technologies used in the design and production of complex final products, including artificial intelligence, machine learning, smart factories, and cyber physical production systems. Our framework contributes to innovation thought and practice by explaining how new product development processes and organizational structures change under the effect of digital technologies.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".