Parametric Modeling of Part Family Machining Process Plans From Independently Generated Product Data Sets
Why this work is in the frame
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Bibliographic record
Abstract
Parametric modeling has become a widely accepted mechanism for generating data set variants for product families. These data sets include geometric models and feature-based process plans. They are created by specifying values for parameters within feasible ranges that are specified as constraints in their definition. These ranges denote the extent or envelope of the product family. Increasingly, with globalization the inverse problem is becoming important: Given independently generated product data sets that on observation belong to the same product family, create a parametric model for that family. This problem is also of relevance to large companies where independent design teams may work on product variants without much collaboration only to later attempt consolidation to optimize the design of manufacturing processes and systems. In this paper we present a methodology for generating a parametric representation of the machining process plan for a part family through merging product data sets generated independently from members of the family. We assume that these data sets are feature-based machining process plans with relationships such as precedences between the machining steps for each feature captured using graphs. Since there are numerous ways in which these data sets can be merged, we formulate this as an optimization problem and solve using the A* algorithm. The parameter ranges generated by this approach will be used in the design of tools, fixtures, material handling automation and machine tools for machining the given part family.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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