Design for Manufacturing of Sculptured Surfaces: A Computational Platform
Bibliographic record
Abstract
This paper presents a computer aided design for machining (DFMc) platform that enables designers to customize the design for the available machine tools and to estimate the effect of design decisions on the accuracy of the final machined products, particularly those containing sculptured surfaces. The platform contains two modules to model and simulate the actual machined surface and to evaluate the resulting minimum deviation zone compared to the desired geometry. In the first module, based on the configuration of the available machine tool and the limitations imposed by its inherent errors, the machined surface is simulated and presented as a nonuniform rational B-spline (NURBS) surface. In the second module, the minimum deviation zone between the actual and the nominal NURBS surfaces is evaluated when the developed method to do this task efficiently improves the convergence of the resulting optimization process. Utilizing this platform, two different applications are developed; design tolerance allocation based on the minimum deviation zone of the machined surface and adaptation of the nominal design to compensate for the effect of machining errors. Employing these applications during the design stage improves the acceptance rate of the produced parts and reduces the rate of scrap and rework. The DFMc platform and its presented applications can be implemented in any integrated computer aided design/computer aided manufacturing (CAD/CAM) system. The presented methods can be applied to any type of input geometries and are particularly efficient for design and manufacturing of precise components with complex surfaces. Products in this group, such as dies and tools, medical instruments, and biomedical implants, mostly have critical and important functionalities that demand very careful design and manufacturing decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".