Closed-loop control of surface preparation for metallizing fiber-reinforced polymer composites
Bibliographic record
Abstract
This study introduces a novel approach to enhance the surface properties of fiber-reinforced polymer composites through thermal spray coatings, utilizing a metal mesh as an anchor to improve coating adhesion. A critical step in this process is achieving optimal exposure of the metal mesh by sandblasting prior to coating. To address this challenge, we propose a closed-loop control system designed to inspect and blast parts effectively. Our method leverages top-view microscope images as inputs, employing a convolutional neural network (CNN) to correlate these images with the corresponding exposure levels of the metal mesh, measured via a destructive method. Upon training, the CNN model accurately estimates the exposure level solely from the top-view images, facilitating real-time feedback to guide subsequent sandblasting operations. Unlike traditional manual inspection methods, which demand expertise and experience, our automated approach streamlines the inspection process using a cost-effective portable digital microscope. Experimental findings validate the efficacy of our method in successfully discerning surface preparation status with an accuracy rate of 95% and demonstrate its practical utility in closed-loop control. Our study not only offers a robust methodology for quantifying surface preparation data but also presents a significant advancement in automating the inspection process. Moreover, the broader implications of our approach extend to various manufacturing sectors, where defect detection and closed-loop control are crucial for optimizing production efficiency and product quality.
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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 | 0.000 | 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) | 0.001 | 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".