Rapid product development in a rapid virtual enterprise - intelligent manufacturing systems realised in a successful european project
Bibliographic record
Abstract
The IMS RPD 2001 (Intelligent Manufacturing Systems Rapid Product Development in a Rapid Virtual Enterprise) project focuses on the acceleration of the product development processes for mechanical parts by providing tools and methods for rapid manufacturing of physical parts. The vision of the project is to accelerate the product development within the described scope by shortening time (increasing the speed) of the respective process cahin, providing parts made of production materials and offering an increased flexibility in tooling technology. The three main objectives are: - The rapid and efficient product realisation in heterogeneous, distributed environments by the use, integration and combination of digital techniques like CAD, FEM, Virtual Reality with physical product realisation techniques like Rapid Prototyping & Rapid Tooling. - The set-up, management and collaboration support of project specific product development environments, called Virtual Enterprises, integrating dispersed partners with their complementing expertise and technology. - Design and manufacturing of parts/tools for adaptive volumes (one of thousands of pieces). The IMS RPD 2001 project is linked to the wolrdwide network of IMS and the interregioanl IMS RPD project. Several other regions like Canada, Australia, USA, Korea and Japan are part of this structure. Regularly held interregional meetings are encouraging communication and collaboration between partners from industrial companies and research institutes on an international level. The intention of this paper is to present the European IMS RPD 2001 project and to point out the benefits of the worldwide collaboration in the field of Rapid Product Development.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".