Effect of Steel Surface Roughness on FBE Coating Performance
Bibliographic record
Abstract
Abstract This paper describes an experimental study of the influence of steel surface roughness on the performance of a fusion bonded epoxy (FBE) pipeline coating. Steel panels were abrasive blast cleaned with various steel shot and grit abrasives. The roughness characteristics of the blast cleaned surfaces were measured with a stylus profilometer, replica tape per NACE SP0287, and a digital replica tape reader. The 3D topographical data files generated by the digital tape reader were interpreted using surface analysis software. A FBE pipeline coating was applied to the prepared steel panels and the performance of the coating was evaluated using pull-off adhesion strength, cathodic disbondment and Atlas cell wet thermal gradient tests. The strength of correlations among the roughness parameters and the FBE coating performance results were compared. Tortuosity, measured with a stylus profilometer, and developed interfacial area (Sdr), measured with the digital replica tape reader, were found to be strongly correlated with FBE coating performance. The findings of this study suggest that the increase in real surface area developed by roughening the surface is a fundamental roughness characteristic that strongly influences coating adhesion performance.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".