DPP: A Distributed Process Planning Approach Using Function Blocks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this research is to develop a new methodology for intelligent and distributed process planning. The primary focus of this paper is on the architecture of the new process planning approach, using function blocks as controller language. The secondary focus is on the other supporting technologies such as machining feature-based design and agent-based decision-making. The methodology proposed for distributed process planning is based on a design-for-machining concept that can seamlessly integrate feature-based design and agent-based planning into function block-based CNC control. Different from traditional methods, the proposed approach has a two-layer structure – supervisory planning and operation planning. The supervisory planning is performed in advance at shop floor level, followed by the operation planning accomplished at run-time at machine level by open CNC controllers. Through decentralization, the distributed process planning shows promise of improving system performance within today’s continually changing shop floor environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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