DFX 2021 32nd SYMPOSIUM DESIGN FOR X
Online via Zoom and Tutzing, Germany. 27th and 28th September 2021. DFX 2021 offers young scientists a platform for presenting new ideas and approaches for improving product development.
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Synthetic Data Generation for Deep Learning Models
Christoph Petroll, Martin Denk, Jens Holtmannspötter, Kristin Paetzold, Philipp Höfer
Type:
Year:
2021
Editor:
Dieter Krause, Kristin Paetzold, Sandro Wartzack
Author:
Series:
DfX
Institution:
The Bundeswehr Research Institute for Materials, Fuels and Lubricants (WIWeB)
Section:
MBSE
Page(s):
10
DOI number:
Abstract:
The design freedom and functional integration of additive manufacturing is increasingly being implemented in existing products. One of the biggest challenges are competing optimization goals and functions. This leads to multidisciplinary optimization problems which needs to be solved in parallel. To solve this problem, the authors require a synthetic data set to train a deep learning metamodel. The research presented shows how to create a data set with the right quality and quantity. It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions. A data set of generic designs is then generated and validated. The generation of the generic design proposals is accompanied by a specific product development example of a drone combustion engine.
Keywords:
