Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates

Paper #:
  • 2015-01-1369

Published:
  • 2015-04-14
DOI:
  • 10.4271/2015-01-1369
Citation:
Liu, K., Tovar, A., Nutwell, E., and Detwiler, D., "Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates," SAE Technical Paper 2015-01-1369, 2015, doi:10.4271/2015-01-1369.
Pages:
12
Abstract:
This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force. The proposed optimization problem is addressed by a multi-objective genetic algorithm on sequentially updated surrogate models, which are optimally selected from a set of 24 surrogates. The effectiveness of the design algorithm is demonstrated on an S-rail thin-walled structure. The best compromised Pareto design increases specific energy absorption and decreases peak crushing force in the order of 8% and 12%, respectively.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$27.00
Mail
$27.00
Members save up to 40% off list price.
Share
HTML for Linking to Page
Page URL

Related Items

Training / Education
1997-05-29
Technical Paper / Journal Article
2011-04-12
Training / Education
1997-05-29
Article
2016-08-15
Technical Paper / Journal Article
2011-04-12