A Deterministic Inspection of Surface Preparation for Metalization
Bibliographic record
Abstract
Abstract To improve the surface properties of fiber-reinforced polymer composites, one method is to employ thermal spray to apply a coating on the composite. For this purpose, it uses a metal mesh serving as an anchor between the composite and the coating to increase adhesion. However, the composite manufacturing covers the metal mesh with resin, and getting an acceptable coating is only possible through an optimum exposure of the metal mesh by sand blasting prior to coating. Therefore, this study aims to develop a computer vision and image processing method to inspect the parts and provide the operator with feedback. Initially, this approach takes the images from a single-view microscope as the inputs, and then it classifies the images into two regions of resin and metal mesh using the Otsu’s adaptive thresholding. Next, it segments the resin areas into distinct connected clusters, and it makes a histogram based on the clusters’ size. Finally, the distribution of the histogram can determine the status of the surface preparation. The state-of-the-art has only examined the sand-blasted composites manually, requiring expertise and experience. This research presents a deterministic method to automate the inspection process efficiently with an inexpensive portable digital microscope. This method is practical, especially when there is a lack of standardized data for machine learning. The experimental results show that the method can get different histograms for various samples, and it can distinguish whether a sample is under-blasted, proper-blasted, or over-blasted successfully. This study also has applications to various fields of manufacturing for defect detection and closed-loop control.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".