A Framework for Integrated Computer-Aided Design, Process Planning and Manufacturing Systems Engineering for Powertrain Machining
Bibliographic record
Abstract
Machined powertrain components are a subset of machined parts that introduce unique and difficult problems to product design, process planning and manufacturing system design for the automotive industry. They are complex, high value-added components that must be produced at large volumes to stringent quality standards. Accordingly product development cycles are typically long. Integrated computer-aided approaches are thus desirable for reducing this time and helping manufacturing engineers design the best process and specify the optimal manufacturing system configuration. This paper presents a framework for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing Systems Engineering (CAE-MS) for producing machined powertrain components. It describes the key components of this framework and in some cases details of the methods and technologies adopted for their realization. This solution is based upon a feature-centric philosophy. This stands in contrast to the product-variant approach that has been common practice in this industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".