GENERIC APPROACH TO PLAUSIBILITY CHECKS FOR STRUCTURAL MECHANICS WITH DEEP LEARNING

Article in Edited Volumes


Publication Details

Author(s): Sprügel T, Schröppel T, Wartzack S
Title edited volumes: Proceedings of the 21st International Conference on Engineering Design (ICED17)
Publisher: Vancouver, Canada, 21.-25.08.2017
Publication year: 2017
Volume: Vol. 1: Resource-Sensitive Design | Design Research Applications and Case Studies
Pages range: 299 - 308
Language: English


Abstract


The simulation of product behavior is a vital part in virtual product development, but currently there is no tool or method available that can examine the quality of FE simulations and decide automatically on whether a simulation is plausible or non-plausible. In the paper a method is presented that enables automatic plausibility checks on basis of empirical simulation datasets. Nodal simulation data is transformed to numerical arrays, of fixed size, using virtual spherical detector surfaces. Afterwards the arrays are used to train a Deep Convolutional Neural Network (AlexNet). The Neural Network can then be used for plausibility checks of FE simulations (structural mechanics). In a first application a Deep Convolutional Neural Network is trained with simulation data of a demonstrator part, the rail of speed inline skates. After the GPU training of the Neural Network, further simulations are evaluated with the net. These simulations were not part of the training data and are used to calculate the prediction quality

of the Neural Network. This approach is to support development engineers during design accompanying FEA in virtual product development.



FAU Authors / FAU Editors

Schröppel, Tina
Lehrstuhl für Konstruktionstechnik
Sprügel, Tobias
Lehrstuhl für Konstruktionstechnik
Wartzack, Sandro Prof. Dr.-Ing.
Lehrstuhl für Konstruktionstechnik


How to cite

APA:
Sprügel, T., Schröppel, T., & Wartzack, S. (2017). GENERIC APPROACH TO PLAUSIBILITY CHECKS FOR STRUCTURAL MECHANICS WITH DEEP LEARNING. In Proceedings of the 21st International Conference on Engineering Design (ICED17) (pp. 299 - 308). Vancouver, Canada, 21.-25.08.2017.

MLA:
Sprügel, Tobias, Tina Schröppel, and Sandro Wartzack. "GENERIC APPROACH TO PLAUSIBILITY CHECKS FOR STRUCTURAL MECHANICS WITH DEEP LEARNING." Proceedings of the 21st International Conference on Engineering Design (ICED17) Vancouver, Canada, 21.-25.08.2017, 2017. 299 - 308.

BibTeX: 

Last updated on 2018-01-09 at 07:11