A Collaborative Scheme for DFX Techniques in Concurrent Engineering Mitigated with Total Design Activity Model
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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