Manufacturing knowledge management based on STEP-NC standard: a Closed-Loop Manufacturing approach
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 paper presents a proposal to ensure Closed-Loop Manufacturing from CNC machines to CAM systems. The main goal is to manage knowledge from the CNC machine and to reuse it in the CAM system. This proposal aims to help CAM programmers for programming new machining sequences by choosing the best CNC machines parameters. The main contribution of this paper focuses on how to provide guidelines extracted from past CNC programs to the CAM programme for future cases. Although STEP-NC standard enhances bidirectional exchanges in the digital chain, from CAD systems to CNC machines, it does not allow the management of manufacturing knowledge. To achieve the information feedback from CNC machine to CAM system, Closed-Loop Manufacturing approach sets up a manufacturing loop using PLM systems supported by OntoSTEP-NC – an ontology based on STEP-NC. Centred on the Manufacturing Process Management platform Closed-Loop Manufacturing is a three-step process: (1) capitalisation of cutting parameters, the manufacturing features and the material to fill the Manufacturing Process Management database, (2) validation of last machining sequences and (3) manufacturing feature recognition to have the most relevant information integration from the Manufacturing Process Management in the CAM programming stage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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