Comparison of Design Automation and Machine Learning algorithms for creation of easily modifiable splines
Editor: Mortensen, N.H.; Hansen, C.T. and Deininger, M.
Author: Gustafsson, Erik Anton; Persson, Johan Alexander; Ölvander, Johan Rolf
Section: Design Support
DOI number: https://doi.org/10.35199/NORDDESIGN2020.55
To enable easy modification and fine tuning of optimization results in a CAD tool by an engineer a flexible representation of the geometry is needed. Two methods based on machine learning and design automation are compared on the task of creating an easily modifiable spline using a few well-placed control points to approximate a center curve representing an optimized hose route.