Fiber Bragg Sensors Embedded in Cast Aluminum Parts: Axial Strain and Temperature Response
Bibliographic record
Abstract
In this study, the response of fiber Bragg gratings (FBGs) embedded in cast aluminum parts under thermal and mechanical load were investigated. Several types of FBGs in different types of fibers were used in order to verify general applicability. To monitor a temperature-induced strain, an embedded regenerated FBG (RFBG) in a cast part was placed in a climatic chamber and heated up to 120 ∘C within several cycles. The results show good agreement with a theoretical model, which consists of a shrink-fit model and temperature-dependent material parameters. Several cast parts with different types of FBGs were machined into tensile test specimens and tensile tests were executed. For the tensile tests, a cyclic procedure was chosen, which allowed us to distinguish between the elastic and plastic deformation of the specimen. An analytical model, which described the elastic part of the tensile test, was introduced and showed good agreement with the measurements. Embedded FBGs - integrated during the casting process - showed under all mechanical and thermal load conditions no hysteresis, a reproducible sensor response, and a high reliable operation, which is very important to create metallic smart structures and packaged fiber optic sensors for harsh environments.
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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 | 0.000 | 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".