A comprehensive evaluation of the low-velocity impact behaviour of intraply hybrid flax/basalt composites using infrared thermography and terahertz time-domain spectroscopy techniques
Bibliographic record
Abstract
Low-velocity impacts severely jeopardize the structural reliability of polymer composites. In view of this, a thorough evaluation of the impact damage of the polypropylene (PP) composites reinforced with an ecofriendly intraply flax/basalt hybrid fabric was performed based on infrared thermography (including pulsed thermography, linear scanning thermography) and terahertz time-domain spectroscopy (THz-TDS) techniques. However, the main problem is the lack of multi-source fusion technique regarding more than two sensors, and the discussions regarding homologous fusion (pulsed thermography and linear scanning thermography), and non-homologous fusion (infrared thermography and THz-TDS). In this work, a comprehensive evaluation for the impact resistance of hybrid polymer composites was conducted, including detecting the uneven resin distribution and exploring new multi-sources fusion strategy. The experimental results demonstrate the superior capability of multi-source fusion techniques. • An improved pulsed phase thermography method is discussed to reduce noise caused by the fibre weaving patterns. This novel technique can increase at least 1/6-fold over the original version. • A new multi-fusion strategy based on Dempster-Shafer theory and unsupervised network U2Fusion is proposed. • A normalized time-domain integration (NTDI) method is presented to efficiently and accurately identify the uneven resin distribution.
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 | 0.001 | 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".