Name
Abstract | ||
|---|---|---|
The Reflection and permeability properties of glass surfaces measured per each wavelength of light directly affects the heat insulation ability and the color of the glass. While the characteristics of these properties can be calculated prior to coating process, the optical properties vary upon the tempering process applied to increase the strength and robustness. The optical effects of the heat treatment that is applied during the tempering process cannot be calculated and modelled by mathematical and analytical approaches. In this paper, a machine learning-based modeling of the effects of the heat is investigated, by making use of the past optical spectral measurement values, obtained before and after the heat treatment. The experiments show that the modeling of the heat treatment effect on glass coating is feasible using artificial neural networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SIU.2019.8806267 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
glass coating,artificial neural networks,heat treatment | Biological system,Tempering,Pattern recognition,Coating,Computer science,Thermal insulation,Robustness (computer science),Artificial intelligence,Treatment effect,Artificial neural network,Chaotic,Wavelength | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benan Akça | 1 | 0 | 0.34 |
| Sinem Eraslan | 2 | 0 | 0.34 |
| Batuhan Gündogdu | 3 | 0 | 0.68 |