A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design
Sauer C, Gerschütz B, Bernsdorf J, Schleich B, Wartzack S (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 2
Pages Range: 1757-1766
DOI: 10.1017/pds.2022.178
Abstract
Supporting product developers in early design phases with Live-Simulation can enhance the quality of early product designs. Live-Simulation can also facilitate a democratization of simulation and puts away pressure from simulation experts. In this paper, a machine learning based quick evaluation tool is proposed to support product developers in interpreting Live-Simulation results. The proposed tool enables a quick evaluation of the Live-Simulation results and enables product developers to further enhance their simulations. The tool is shown within a use case in bike rocker switch design.
Authors with CRIS profile
Christopher Sauer
Chair of Engineering Design
Benjamin Gerschütz
Chair of Engineering Design
Benjamin Schleich
Chair of Engineering Design
Sandro Wartzack
Chair of Engineering Design
Related research project(s)
Involved external institutions
Germany (DE)How to cite
APA:
Sauer, C., Gerschütz, B., Bernsdorf, J., Schleich, B., & Wartzack, S. (2022). A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design. Proceedings of the Design Society, 2, 1757-1766. https://doi.org/10.1017/pds.2022.178
MLA:
Sauer, Christopher, et al. "A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design." Proceedings of the Design Society 2 (2022): 1757-1766.
BibTeX: Download