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ISSN Online: 2377-424X

ISBN Print: 978-1-56700-421-2

International Heat Transfer Conference 15
August, 10-15, 2014, Kyoto, Japan

Effect of Structure and Transfer Function on Artificial Neural Networks Used in Radiation Thermometry for Steel

Get access (open in a dialog) DOI: 10.1615/IHTC15.rad.008683
pages 7251-7263

Sinopsis

The effect of structure and transfer function on Artificial Neural Networks used in radiation thermometry is discussed in this study. The spectral emittance was inferred for six types of steels (AISI 420, AISI 630, AISI A2, AISI A6, AISI H10, AISI H13) at three temperatures (700 K, 800 K and 900 K). The sample temperatures were predicted simultaneously to comprehend the effect of structure and transfer function on temperature prediction and then the better structure of neural networks was found. In ANNs algorithm, the sample’s spectral intensity measured by spectrometer was used as revised data to derive the modified relationships for the weights and biases by numerical method. Then, the surface emittance and temperature could be inferred after incessant learning and modification. Besides, the accuracy of this thermometry was examined by the comparison between real and inferred emittance. Temperature prediction by using the better structure of neural networks found in this study was compared to the multispectral radiation thermometry and the accuracy was discussed. Finally, the inferred temperatures for open-air and high-vacuum experiments were compared to find out the oxidation effect on this thermometry.