Deep learning-based metamodeling can approximate complex engineering systems based on design data, but it has limitations in acquiring a large amount of data through experiments or simulations. When the design data for metamodeling are insufficient, data can be generated through generative models of deep learning. This study proposes a deep learning-based efficient metamodeling method called domain knowledge-integrated designable data augmentation (DDA) with transfer learning for engineering design. The DDA is a metamodel that applies an inverse generator to existing data augmentation algorithms. Virtual responses can be generated using a small number of actual responses to predict the performance of an engineering system and estimate the design variables that affect the generated virtual responses. Moreover, a rapid and accurate design can be achieved by applying transfer learning and domain knowledge-based learning to DDA. The proposed algorithm was applied to the design of a bumper considering vehicle crash safety. As a result, virtual responses that were approximately 95% similar to the actual responses were generated, and design solutions were derived. In addition, the validity of the proposed algorithm was verified by comparing it with existing metamodels. Graphical abstract: [Figure not available: see fulltext.].
|Journal||Structural and Multidisciplinary Optimization|
|Publication status||Published - 2022 Jul|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea [Grant No. 2022R1A2C2011034]. This work was supported by 'Development of Prognostics and Health Management Based on Multivariate Analysis of Autonomous Vehicle Parts [Grant No. 20018208].
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Control and Optimization
- Computer Graphics and Computer-Aided Design