Prof. Dr. Gordon Elger
Head of Fraunhofer Application Center "Connected Mobility and Infrastructure"; Research Professor Assembly and Connection Technology
Project description
- Predicting solder joint reliability with machine learning approaches
- Improved training of AI models through material science and physics insights, specifically Physically Informed Neural Networks
- Inclusion of a large aging data set with TTA, SAM, in-situ stress measurements for understanding aging and training of the models
- Material science investigation of the solders and the solder joints by shear creep tests and nanoindentation at different agings
Project information
| Tasks THI | Creation of the dataset (aging+measurements), Development of AI models, FE-Simulation |
| Project partner | Conti Temic Microelectronics GmbH, XITASO GmbH IT & Software Solutions, mts Consulting & Engineering GmbH, Technische Hochschule Ingolstadt |
| Project sponsor | Bayerisches Verbundforschungsprogramm Förderlinie "Digitalisierung", Bayerisches Staatsministerium für Wirtschaft |
| Project term | 09/01/2021 to 29/02/2025 |