Bibliographic record
Abstract
This study was performed to validate a simulated model of an automotive factory spray paint gun. The project ensures that repeatable and valid data are obtained in the simulation of a paint process. A simulated paint gun model was created using DELMIA IGRIP simulation software; an interactive, 3D graphic simulation tool for designing, optimizing, and programming robotic paint booths off-line. IGRIP is used to generate optimized robot paths using the workpiece CAD geometry and download robot motion and process programs. A Design of Experiment (DOE) study was executed to validate the simulated paint gun model. The DOE was performed using a physical paint robot and a virtual paint robot. Analysis of Variance (ANOVA) was performed on the two experiments in order to detect any differences in average performance of the paint process parameters tested. Understanding the contribution of each factor was significant to determine the validity of the simulation. The comparison of the outputs of the two experiments provided an assessment and validation of the simulated paint gun model. The IGRIP simulation software is limited in its abilities to quantify expected improvements of all paint quality characteristics in that it is not able to consider parameters of viscosity, humidity, temperature, air velocity. The IGRIP model that was developed must be calibrated to mimic the physical process results. This thesis advances the Simulation and Off-Line Programming project as it supports the development of a robust design for the paint process simulation. Source: Masters Abstracts International, Volume: 40-03, page: 0770. Adviser: Peter R. Frise. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".