Machined Feature Estimation and Inspection for Internet-Based Manufacturing System
Bibliographic record
Abstract
With easy access of the Internet, the real-time, web-based collaborations are impacting the current practices of product design and manufacturing. In this research, an Internet-based manufacturing design system has been developed based on the collaborations of several partners located thousands of miles away from each other. This paper introduces a sub-system of this Internet-based manufacturing system for machined feature estimation and inspection using 3D solid models. Using the engineering data of cutting processes received from our collaborator, through the broadband Internet, taking into account of the factors like cutting forces and tool deflections, this sub-system determines the material removal processes by subtracting the tool cutting volume from work-piece raw material. It creates the machined 3D solid geometry models by simulating the machining processes. The estimated geometry is then taken to a simulated inspection process for engineering analysis and CNC machining program verification. To simulate the inspection process on a Coordinate Measuring Machine (CMM), a series of rays is shot to the machined surface to simulate the CMM probe touching for acquiring surface data. The simulated measurement data is then localized to design surface and comparison between them is made to quantify the machining errors. Through the Internet, the geometrical features and inspection result can be visualized and shared by other sub-systems of the whole manufacturing system based on the commercial CAD data formats. The operation of this subsystem and related facilities is local execution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".