Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates 2015-01-1369
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.
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, https://doi.org/10.4271/2015-01-1369. Download Citation
Author(s):
Kai Liu, Andres Tovar, Emily Nutwell, Duane Detwiler
Affiliated:
Purdue University, Indiana Univ Purdue Univ, Honda R & D Americas Inc
Pages: 12
Event:
SAE 2015 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Machine learning
Optimization
Mathematical models
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