1. Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag
- Author
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Arjomandi Rad, Mohammad, Cenanovic, Mirza, and Salomonsson, Kent
- Subjects
data-driven design ,Product development ,Computational Mathematics ,Annan maskinteknik ,Datavetenskap (datalogi) ,image regression ,Beräkningsmatematik ,Computer Sciences ,dynamic relaxation ,convolutional neural networks ,General Engineering ,Other Mechanical Engineering - Abstract
Today digital qualification tools are part of many design processes that make them dependent on long and expensive simulations, leading to limited ability in exploring design alternatives. Conventional surrogate modelling techniques depend on the parametric models and come short in addressing radical design changes. Existing data-driven models lack the ability in dealing with the geometrical complexities. Thus, to address the resulting long development lead time problem in the product development processes and to enable parameter-independent surrogate modelling, this paper proposes a method to use images as input for design evaluation. Using a case study on the curtain airbag design process, a database consisting of 60,000 configurations has been created and labelled using a method based on dynamic relaxation instead of finite element methods. The database is made available online for research benchmark purposes. A convolutional neural network with multiple layers is employed to map the input images to the simulation output. It was concluded that the showcased data-driven method could reduce digital testing and qualification time significantly and contribute to real-time analysis in product development. Designers can utilise images of geometrical information to build real-time prediction models with acceptable accuracy in the early conceptual phases for design space exploration purposes. CC BY-NC-ND 4.0Copyright © 2023 Informa UK LimitedCONTACT Mohammad Arjomandi Rad radmo@chalmers.seReceived 20 Oct 2022, Accepted 29 Dec 2022, Published online: 19 Jan 2023The authors would like to acknowledge the staff in Autolivin Sweden for their participation in the project and also the Swedish Knowledge Foundation (KK-Stiftelsen with grant number 20180189) for the financial support.
- Published
- 2023