Manufacturing Process Management: iterative synchronisation of engineering data with manufacturing realities
Bibliographic record
Abstract
The principles of Concurrent Engineering (CE) have led to an early introduction of manufacturing decisions in the Product Development Process (PDP). Nevertheless, the integration along the product life cycle of computer tools to help engineers manage their tasks in the global market still suffers from a poor understanding of information requirements for the effective streamline of the design to production process. Manufacturing Process Management (MPM) is a strategy that supports formal communication between engineering and production in a virtual 3D environment. This paper outlines how MPM enables a real-time assessment of component manufacturability and a parallelisation of product design and manufacturing processes. The proposed scheme is dedicated to offer CE teams the answers to integrated change management issues through a digital collaborative environment. From a technological perspective, a MPM solution provides an intelligent bridge between the Computer-Aided Design/Product Data Management (CAD/PDM) and Enterprise Resource Planning/Manufacturing Execution System (ERP/MES) software with viable perspectives for complete Product Life cycle Management (PLM) packages and new Knowledge Management (KM) approaches.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".