Transitioning additive manufacturing from rapid prototyping to high‐volume production: A case study of complex final products
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
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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