34th Design for X Symposium (DFX 2023)
DfX offers young scientists from the field of product development a platform for presenting new ideas and approaches to their research. (Abstract Submissions by 26 May 2023)
Knowledge Base Repository
In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.
Using machine learning to increase efficiency in design of experiments for cyclic characterization of fibre-reinforced plastics
Marc Gadinger, Christian Witzgall, Thomas Hufnagel, Sandro Wartzack
Type:
Year:
2023
Editor:
Dieter Krause, Kristin Paetzold-Byhain, Sandro Wartzack
Author:
Series:
DfX
Institution:
Friedrich-Alexander-Universitat Erlangen-Nurnberg, Engineering Design, Germany
Page(s):
115-124
DOI number:
Abstract:
Efficient characterization of fatigue behavior plays a crucial role in engineering design as it reduces the financial costs associated with expensive experimental tests. Existing methods for characterizing the fatigue behavior of fibre-reinforced plastics have proven inefficient due to the oversight of important design parameters, such as fibre orientations. To address this challenge, we propose an innovative approach based on Gaussian process regression. Our approach integrates previously unaccounted design parameters into the decision-making process, ensuring that optimal design points are selected for testing. By doing so, we maximize the gain of knowledge within the model, resulting in improved efficiency and accurate characterization of fatigue behavior.
Keywords:
