Please use this identifier to cite or link to this item: https://doi.org/5/019
Title: Artificial neural network model for material characterization by indentation
Authors: Tho, K.K. 
Swaddiwudhipong, S. 
Liu, Z.S.
Hua, J. 
Issue Date: Sep-2004
Source: Tho, K.K.,Swaddiwudhipong, S.,Liu, Z.S.,Hua, J. (2004-09). Artificial neural network model for material characterization by indentation. Modelling and Simulation in Materials Science and Engineering 12 (5) : 1055-1062. ScholarBank@NUS Repository. https://doi.org/5/019
Abstract: Analytical methods to interpret the indentation load-displacement curves are difficult to formulate and solve due to material and geometric nonlinearities as well as complex contact interactions. In this study, large strain-large deformation finite element analyses were carried out to simulate indentation experiments. An artificial neural network model was constructed for the interpretation of indentation load-displacement curves. The data from finite element analyses were used to train and validate the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with the load-displacement curves that were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation load-displacement curve to the elasto-plastic material properties.
Source Title: Modelling and Simulation in Materials Science and Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/50672
ISSN: 09650393
DOI: 5/019
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