Laser Light Transmission Through Thermoplastics as a Function of Thickness and Laser Incidence Angle: Experimental and Modeling
Bibliographic record
Abstract
It is important to accurately measure and predict the laser light transmission through unreinforced and reinforced thermoplastics if candidate materials are to be assessed for laser transmission welding (LTW) applications. This paper presents the results of laser transmission measurements through unreinforced polyamide 6 (PA6) and 10% glass fiber reinforced polycarbonate of various thicknesses and corresponding to various laser incidence angles (angle between the incident laser beam and the normal to the transparent part). A novel transmission measurement method, developed by the authors, was employed. A model, utilizing the Fresnel specular surface reflection conditions as well as accounting for refraction, absorption and reflection of the laser light through the bulk material, was used to predict transmission as a function of thickness and laser incidence angle. Results of transmission tests on both materials showed that, for a given thickness, the transmission decreases as the laser angle of incidence increases. In addition, at any given laser incidence angle, the transmission decreases as the thickness increases. The advantage of the model is that it requires only one experimentally determined constant for a given material. Good agreement existed between the experimentally measured transmission and the model prediction for the range of thicknesses and laser incidence angles studied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".