Please use this identifier to cite or link to this item: https://doi.org/6/013
Title: Material characterization via least squares support vector machines
Authors: Swaddiwudhipong, S. 
Tho, K.K. 
Liu, Z.S.
Hua, J. 
Ooi, N.S.B.
Issue Date: 1-Sep-2005
Source: Swaddiwudhipong, S.,Tho, K.K.,Liu, Z.S.,Hua, J.,Ooi, N.S.B. (2005-09-01). Material characterization via least squares support vector machines. Modelling and Simulation in Materials Science and Engineering 13 (6) : 993-1004. ScholarBank@NUS Repository. https://doi.org/6/013
Abstract: Analytical methods to interpret the load indentation curves are difficult to formulate and execute directly due to material and geometric nonlinearities as well as complex contact interactions. In the present study, a new approach based on the least squares support vector machines (LS-SVMs) is adopted in the characterization of materials obeying power law strain-hardening. The input data for training and verification of the LS-SVM model are obtained from 1000 large strain-large deformation finite element analyses which were carried out earlier to simulate indentation tests. The proposed LS-SVM model relates the characteristics of the indentation load-displacement curve directly to the elasto-plastic material properties without resorting to any iterative schemes. The tuned LS-SVM model is able to accurately predict the material properties when presented with new sets of load-indentation curves which were not used in the training and verification of the model. © 2005 IOP Publishing Ltd.
Source Title: Modelling and Simulation in Materials Science and Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/65787
ISSN: 09650393
DOI: 6/013
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