Metal Embedded Optical Fiber Sensors: Laser-Based Layered Manufacturing Procedures
Bibliographic record
Abstract
This paper describes laser-based layered manufacturing processes for embedding optical fiber Bragg gratings (FBGs) in metal structures to develop cutting tools with embedded sensors. FBG is a type of optical fiber that is used for the measurement of parameters manifesting as the changes of strain or temperature. The unique features of FBGs have encouraged their widespread use in structural measurements, failure diagnostics, thermal measurements, pressure monitoring, etc. Considering the unique features of FBGs, embedding of the sensors in metal parts for in-situ load monitoring is a cutting-edge research topic with a variety of applications in machining tools, aerospace, and automotive industries. The metal embedding process is a challenging task, as the thermal decay of UV-written gratings can start at a temperature of ∼200 °C and accelerates at higher temperatures. The embedding process described in this paper consists of low temperature laser microdeposition of on-fiber silver thin films followed by nickel electroplating in a steel part. A microscale laser-based direct write (DW) method, called laser-assisted maskless microdeposition (LAMM), is employed to deposit silver thin films on optical fibers. To attain thin films with optimum quality, a characterization scheme is designed to study the geometrical, mechanical, and microstructural properties of the thin films in terms of the LAMM process parameters. To realize the application of embedded FBG sensors in machining tools, the electroplating process is followed by the deposition of a layer of tungsten carbide-cobalt (WC-Co) by using laser solid freeform fabrication (LSFF). An optomechanical model is also developed to predict the optical response of the embedded FBGs. The performance of the embedded sensor is evaluated in a thermal cycle. The results show that the sensor attains its linear behavior after embedding. Microscopic analysis of the tool with the embedded sensor clearly exhibits the integrity of the deposited layers without cracks, porosity, and delamination.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".