Quantitative Infrared Thermography (IRT) and Holographic Interferometry (HI): Nondestructive Testing (NDT) for Defects Detection in the Silicate Ceramics Industry
Bibliographic record
Abstract
Ceramics are inorganic materials fabricated by a high-temperature chemical reaction. Most ceramics are oxides, but the term is also used for silicides, nitrides and oxynitrides, hybrids and other inorganic materials. It is convenient to consider ceramics that are essentially silicates, called traditional ceramics, separately from all of the others. In the ceramic industry testing systems are seldom employed for detecting on-line the presence of defects in ceramic tiles. Defects in the ceramic body are usually originated during the pressing stage due to the incorrect use of process parameters or to the improper selection of raw materials. These defects are generally characterized by the inclusion of heterogeneous materials or agglomerates, which decreases the structural strength jeopardizing the final quality of the produced piece. Disagreeable repercussions on the sale market, especially on higher-quality ceramics such as porcelain, are characterized by the lack of specific nondestructive testing (NDT) techniques that “certify”, pre-emptive, the quality of the produced piece. The integration of IRT and HI for the NDT of a green ceramic tile with both fabricated and real defects (cracks caused by the natural shrinkage process), allowed us to produce a clear “defects map”.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".