A System Design for Computer-Aided Manufacturing Shared, Interactive Process Setup Sheets
Bibliographic record
Abstract
Computer-aided manufacturing (CAM) software is used to develop a process plan, which consists of an operations list, tool paths, tooling, process parameters, and depending on the system, material handling operations. Upon completing the development of a process plan, setup sheets are generated for the personnel involved in the setup, production, testing, and product validation activities for a product. Typically, this documentation is in a hardcopy format, or is a static electronic document, and the direction of the communication is unidirectional — from the process planner to the support personnel. With the ubiquitous communications tools available to individuals today, a more sophisticated approach should be taken to transmit, store, and communicate changes to and from the shop floor. Presently, standard setup documentation consists of the project information utilized for the developed process plan. Pictures such as screen captures of the tool path, virtual verification images, and physical elements such as specialty tools may be included. However, modifications are made continuously to improve the cycle time, quality, or to adjust for other product or process changes. This research focuses on the development of interactive setup sheets that utilize existing desktop CAD/CAM software and mobile technologies, with the potential for leveraging the advantages of manufacturing cloud computing. Videos, links to additional documentation, and the ability to edit a subset of process parameters such as a tool diameter are incorporated. The operator is able to physically change tools or other key process setup information, and then send the information to the CAM system in order to regenerate the updated tool paths and documentation. Complementing the flexible, agile, and reconfigurable paradigms is the communication flexibility provided by fast wireless networks along with, cloud computing resources that can accessed with mobile devices, which are ubiquitous in today’s society. This technology that has not yet been heavily employed in the manufacturing environment, and research leveraging these new tools need to be explored.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".