A Multi-Agent System for Distributed, Internet Enabled Cutter/Workpiece Engagement Extractions
Bibliographic record
Abstract
Cutter/workpiece engagement (CWE) extraction is an important problem in process modeling. One approach is to use a B-rep solid modeler to perform the calculations. However, this can have a high computational overhead especially for complicated workpieces. This paper presents a multi-agent system for B-rep based CWE extraction that allows distributed processing of the modeling steps over the Internet. The CWE calculation utilizes distributed agents for performing swept volume and removal volume construction in addition to the extraction of the CWE geometry itself. These distributed agents provide the capability to perform many of the calculations in parallel. The proposed methodology thus makes the best use of available, distributed computing resources leading to greatly improved efficiency in the CWE calculations. If agents are available to perform these calculations using other non-B-rep approaches the proposed framework facilitates their integration. This paper presents the architecture of the framework. This requires a specification of each agent in the framework. The mechanisms adopted for the three primary agent actions, task scheduling, master agent selection and results passing are presented. Interaction protocols are provided to explain how agents cooperate with each other to achieve parallel computation. Finally a prototype implementation and an example are given to show the effectiveness and efficiency of the system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".