Geometric data of a requested forging is important as a source to estimate feasibility and offer realistic pricing. However, every bigger deviation in such calculation regarding technical viability costs involved companies’ possible revenue. 

To mitigate this issue and support the technologists and sales department an autoencoder (unsupervised learning) with an attached regression model was developed (pre-existing). Nevertheless, this system still needs adaptation/improvement to meet the operational requirements. 

This bachelor thesis proposes a way to implement an optimization process for adjusting the layer structure and possible scaling of a given autoencoder system. The autoencoder itself uses 3D surface data in form of a “.stl” to create a point cloud in x, y, and z. A docker image containing the autoencoder then extracts the most significant 3D features and provides an estimation for feasibility and price. The focus lies on creating a wrapper function to test different hyperparameters in an automated way. Strategies like random search, grid search, and Bayesian optimization will be applied. The results of the optimized framework will be challenged with the pre-existing autoencoder model.