A New Evaluation Method for Surface Finish of Composite Automotive Panels Using Waveform Analysis
Bibliographic record
Abstract
The surface finish of composite plates made using Resin Transfer Molding of glass/polyester is studied. A surface profilometer is used to measure the surface profile and to record raw data. The objective is to develop an objective method (rather than using human judgment) to differentiate the quality of one surface from another. Initially commonly used current techniques are utilized to assess the quality of the surface finish. These include subjective evaluation through a survey of observations from a group of people; the use of the average amplitude of the signals, frequency analysis, and filtering. For surfaces that have approximate quality, human visual observation can differentiate the quality between the surfaces, but the objective methods (average amplitude, frequency spectrum, and filtering) cannot. A new objective technique is found to be able to distinguish surfaces of approximate quality. This uses the comparison between the parameters of a reference good surface to those of the surface under consideration. A comparative index can be obtained to indicate the degree of similarity between the surface under study and the reference (good) surface.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".