Faezeh Nejati

• Since 04/2020:
  Ph.D. researcher at the Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
• 10/2015 – 03/2020:
  M.Sc. student in Medical Engineering, Medical Image and Data Processing, at Friedrich-Alexander-Universität Erlangen-Nürnberg
  Master Thesis: ‘Atrial Fibrillation Classification from Short Single-Lead ECG Signals Using Deep Neural Networks’
• 09/2008 – 01/2013:
  B.Sc. student in Information Technology Engineering, at Islamic Azad University of Mashhad, Iran
  Bachelor Thesis: ‘Implementation of Lync server 2010’

2020

• Improved Determination of the Failure Behavior of Sheet Metals Using Deep Learning
  (Third Party Funds Single)

        Term: April 15, 2020 – October15, 2021
        Funding source: DFG-Einzelförderung

 Abstract 

The growing interest in CO2 emission reduction, low usage of petrol, and complex design of automobiles has led the automotive industry to think of using new, high-strength, lightweight materials that differ significantly from the conventional ones. Deep learning has shown great potential in computer vision and image analysis applications. Hence, it would be interesting to incorporate deep learning in the sheet metal formation analysis application. This project proposes to exploit deep learning methods for automatic extraction of the Forming Limit Curve (FLC) to correctly defining the forming capacity of the new materials.