AI-driven modelling of electrostatic powder coating
Bibliographic record
Abstract
Powder coating has become a popular surface finishing technique in many industries (e.g., home appliances, automotive parts, outdoor products) owing to its durability, corrosion resistance and low environmental impact. While powder recycling systems are often in place to capture non-deposited powder, waste can nonetheless be further reduced by optimizing the uniformity of the applied coating thickness. In this work, an experimental data collection approach is proposed to generate a high-quality database for the training of an AI-driven model of the distribution of electrostatic powder coating on flat surfaces. We propose a novel, scalable, low-cost automated coating thickness measurement system based on a microscopic incision tool, an open hardware CNC machine, a Raspberry Pi and the open source OpenCV image processing library. The system is capable of characterizing the coating thickness distribution of flat plates at a custom spatial resolution (as low as 0.1 mm) in a reasonable time with an accuracy of 2 µm. The proposed system can serve as a quality control and process optimization tool in an industrial workflow.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".