A Collaborative Scheme for DFX Techniques in Concurrent Engineering Mitigated with Total Design Activity Model
Bibliographic record
Abstract
Industry 4.0 has sparked rapid changes in the manufacturing and construction sectors, leading to a significant shift in how off-site factory-based panelized construction machines are designed and manufactured. Concurrent engineering which seeks to close the gap between design and manufacturing sectors provides an ideal environment for machine development. It is a systematic methodology to integrate machines holistic concurrent design activities and their related processes. Competition arising in the marketplace for newly developed machines is driving modifications in the way machine designers develop production machines. Thus, to boost the efficiency in concurrent machine development, appropriate evaluation, and decision analysis tools required to be developed and utilized. Currently, there is no DFX selection tool available to aid the designer in concurrent machine design applications. In this paper, these challenges are addressed through a comprehensive qualitative literature review of DFX techniques with their implementation in Stuart Pugh: Total Design Activity Model. Various DFX techniques are mapped and clustered in a collaborative scheme, interactions and links between them are identified, and the relative importance weight of each is calculated. A description of a functional DFX scheme is proposed in this paper that can aid designers in establishing lean design processes for machine development and reveals its potential application in Multi-DFX fuzzy multi-criteria decision-support system.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".