Evaluating thermal properties of sugarcane bagasse-based composites by using active infrared thermography and terahertz imaging
Bibliographic record
Abstract
This study is devoted to the analysis of impact damage in composite samples by applying techniques of IR and Terahertz imaging. The samples made of high-density homogeneous particleboards of sugarcane bagasse and castor oil polyurethane resin were subjected to impacts with the energy from 5 to 30 J. The impact damage defects were evaluated by applying one- and two-sided thermal non-destructive testing. Both the apparent thermal effusivity and diffusivity were determined to analyse their relationship to impact energy. In the above-mentioned range of impact energies, the thermal effusivity varied from 5 to 18%, while the variations in thermal diffusivity were from 4 to 24%. The algorithm of dynamic thermal tomography was used to demonstrate that predominant subsurface cracking occurred at depths up to 1 mm. In addition, the defect areas were tested by applying the Terahertz technique, whose results qualitatively matched the IR thermographic ones to show that the damaged areas were larger than they appeared visually. However, both inspection techniques have revealed no significant dependence of analysed parameters on impact energy. This is believed to prove that non-uniform composite structure is the decisive factor in producing structural defects under impacting.
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 | 0.000 |
| 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".