The novel equipment for measuring internal stress in acrylic
Bibliographic record
Abstract
Abstract Acrylic is widely used in aquariums, windows of planes and submarines, and even scientific experiments like the Sudbury Neutrino Observatory (SNO) and the Jiangmen Underground Neutrino Observatory (JUNO). Internal stress has a significant impact on the properties and characteristics of acrylic such as strength, fracture and creep, which is highly concerned and has become a hotspot in research field. The measurement of internal stress is an important issue, which includes two aspects - the calibration of stress-optical coefficient of acrylic and the measurement of birefringence optical path difference (BOPD) caused by internal stress. The measuring equipment mainly realize the measurement of BOPD, and currently have the largest dynamic range of 50–3000 nm. Dynamic range is considered as one of the core performance indicators of measuring equipment, and a larger dynamic range is urgently required to meet the needs of different scenarios. The novel equipment for measuring internal stress in acrylic has been designed and developed based on photo-elastic principle and spectrometric method, which has the dynamic range of 20–12000 nm and the uncertainty of stress measurement better than 3%. The measuring principle, components, functions and measurements of the novel measuring equipment are introduced and discussed in this article.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".