Master Thesis: »Deep learning for defect detection in battery cells«

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world's leading applied research organization. Around 30 800 employees work with an annual research budget of 3.0 billion euros.

At the Fraunhofer IPT in Aachen, we work with more than 530 employees every day to make the production of the future more digital, more flexible, and more sustainable. In the department »Production Quality« we apply digital technologies to optimize production processes by using artificial intelligence to make production more sustainable. One focus of our work is on optimizing the production processes for lithium-ion battery cells and fuel cells. Within the scope of your thesis, you will investigate deep learning-based defect detection approaches. To this end, we use roll-to-roll processes for the efficient coating of electrodes. However, defects can occur during this process step, causing high reject rates. To solve this problem, we are developing a modern deep learning-based defect detection system. A central step for the application of this system is the reduction of the annotation effort by process experts. Transfer learning approaches for deep learning models are a promising way to reduce this effort. Therefore, within the scope of this master's thesis, different deep-learning approaches will be implemented and evaluated., With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Ihre Aufgaben

• Selection of suitable deep-learning approaches in the field of transfer

• Training of deep learning models for defect detection and analysis of the results

• Implementation of suitable deep learning models in the defect detection system

Ihr Profil

• You are studying mechanical engineering, computer science, CES… or a comparable subject

• First experience with PyTorch and Deep Learning is favorable

• A high degree of initiative, independence, and problem solving skills

  What you can expect

• Ideal conditions for practical experience alongside your studies

• GPU server for data science applications and for working efficiently with large models

• Flexible working to combine study and job in the best possible way

Kontakt

For any further information on this position please contact: Alexander Kreppein, M.Sc. Research fellow in the department »production quality« Phone: +49 241 8904-289

Fraunhofer Institute for Production Technology IPT

www.ipt.fraunhofer.de