Student (f/m/x) - ML-Based Composite Panel Prediction for Wing Preliminary Design
German Aerospace Center (DLR)
Braunschweig, Germany
Internship Explorer
Internship Explorer
Composite structures are key to lightweight aircraft design but require complex optimization to balance stiffness and mass. Conventional analysis of composite panels under varying loads is computationally intensive. By generating a dataset of optimized CLT plates with different dimensions and load cases, a machine learning model can be trained to predict stiffness and mass directly. This enables rapid structural assessments of full wing assemblies without detailed sizing. The objective of this thesis is to develop and train a machine learning–based surrogate model to predict the stiffness and mass of composite panels as a function of geometry and loading conditions. You will be responsible for the development of your solution from conception to final implementation. As part of a student job or internship, you will work 10-20 hours per week. In general, it would be desirable to combine this activity with a student research project or thesis.
