Liu, KaiTovar, AndresNutwell, EmilyDetwiler, Duane2019-01-082019-01-082015-04Liu, K., Tovar, A., Nutwell, E., & Detwiler, D. (2015). Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates. Presented at the SAE 2015 World Congress & Exhibition. https://doi.org/10.4271/2015-01-1369https://hdl.handle.net/1805/18103This 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.enPublisher Policythin-walled structuresmachine learningmultiple surrogatesConference proceedings