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Machine Learning in Glass Technology

Machine Learning in Glass Technology. Batuhan Gündoğdu. Case Study. Neural Network-Based Modeling of Heating Process in Optical Spectra. Why Machine Learning?. Used to model chaotic processes or phenomena that can not be analytically explained. Examples. Examples. Examples. Examples.

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Machine Learning in Glass Technology

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  1. Machine Learning in Glass Technology Batuhan Gündoğdu

  2. Case Study Neural Network-Based Modeling of Heating Process in Optical Spectra

  3. Why Machine Learning? • Used to model chaotic processes or phenomena that can not be analytically explained

  4. Examples

  5. Examples

  6. Examples

  7. Examples

  8. Examples

  9. The ‘Learning’ • 3 Requisites • Data • Pattern • No availability of analytic solution

  10. The ‘Learning’

  11. Artificial Neural Networks

  12. Artificial Neural Networks

  13. Artificial Neural Networks “DOG”

  14. Artificial Neural Networks “DOG”

  15. Artificial Neural Networks “DOG”

  16. Artificial Neural Networks

  17. Artificial Neural Networks

  18. Artificial Neural Networks DOG! Supervised Learning

  19. Back to Machine Learning in Glass Technologies

  20. GOAL • Model the unpredictable effects of heating process • Avoid employing the heating process, since we will know what optical spectra toexpect

  21. Modeling Heating Process

  22. Modeling Heating Process

  23. Modeling Heating Process

  24. Modeling Heating Process

  25. Modeling Heating Process

  26. Modeling Heating Process • Input: T, Ru, Rc and lambda before heating • Output: T, Ru and Rcafter heating

  27. ANNs for Modeling • Two Layer Neural Network with ReLu activations • Batch Normalization of Inputs • Keras Library, Python Code

  28. Results

  29. Results

  30. Results

  31. What’s Next? • Incorporating coating features as input to better generalizing to new models • Prescriptive training on coating layer design, for a desired optical spectra

  32. Thank you for listening

  33. Questions?

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