1 / 33

Machine Learning in Glass Technology

Explore the utilization of artificial neural networks to model chaotic heating processes in glass technology, aiming to predict optical spectra changes without needing physical heating. This case study details the model development and future steps for enhanced predictions.

rlucky
Download Presentation

Machine Learning in Glass Technology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related