Method to obtain hybrid rapid tools with elementary component assembly
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a method to obtain hybrid rapid tools with elementary component assembly. Design/methodology/approach The authors' method proposes a functional representational model, starting with the product features, analyzed from three points of view: a feasibility analysis; a manufacturing analysis; and an assembly and synthesis analysis. This method, based on CAD STEP AP‐224 data, makes it possible to obtain an exhaustive list of solutions for the module. The work is illustrated with an industrial example. To construct the Assembly Identity Card (AIC) and test the various parameters that influence the quality of the injected parts, a hybrid injection mold has been produced. The methodology associated with the use of this AIC uses a “representation graph”, which makes it possible to propose a set of valid solutions for assembling the various tooling modules. This method is validated by industrial example. Findings The product part is decomposed into a multi‐component prototype (MCP), instead of being made as a single part, which optimizes the manufacturing process and enables greater reactivity during the development of the product. Research limitations/implications The final goal is to propose a software assistant used in association with CAD system during the design of hybrid rapid tooling. An important work concerning the features recognition must be implemented. The assembly of the different parts of the hybrid rapid tooling must be considered and optimized. Practical implications This method allows the selection of the best process technologies from manufacturing tools. Originality/value The analysis of manufacturing hybrid rapid tooling has not been studied previously.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".