ATTRIBUTE-BASED DESIGN DESCRIPTION SYSTEM IN DESIGN FOR MANUFACTURABILITY AND ASSEMBLY
Bibliographic record
Abstract
Present computer-aided design (CAD) systems, intentionally developed as detail oriented designing tools, do not fully support the activities at the early stage of product development. CAD systems, which require a detailed level of design, prohibit the creative and free expression of a design idea. The solution to the limitations of present CAD systems is to fully utilize the graphical ability of current computer systems to represent a design with an easily understood design description in the conceptual design stage. We have developed a computerized product development tool to support designing activities in the conceptual design phase. The attribute-based design description system (ADDS) is a feature-based system that incorporates life-cycle engineering analysis and solid modeling to form an integrated CAD system. It provides a simple design representation interface and assembly modeling, evaluates the design for life-cycle engineering issues, and exports the design to AutoCAD as a solid model with flexible information input requirements. The research thus provides a starting point to the development of CAD systems that support productivity in the conceptual design stage. ADDS has been validated by describing three different design examples of power transmission systems in ADDS and exporting them to AutoCAD. This paper examines the benefits of applying a specification driven approach and presents a framework for environments that can support the related design activities. The Design Analysis and Simulation Environment (DASE) based upon this framework has been successfully implemented through a joint initiative between Bell Canada and McGill University.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".