Papers by Author: Xiao Xiang Su

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Authors: Nyoman Budiarsa, Andrew Norbury, Xiao Xiang Su, Gareth Bradley, Xue Jun Ren
Abstract: In this work, the indentation size effect (ISE) in Vickers hardness tests of steel with selected heat treatments (annealed or tempered) has been investigated and analysed. Systematical hardness tests were performed within a commonly used micro-load range. The experimental data was analysed according to the Meyer power-law and the proportional specimen resistance (PSR) models and the link between ISE and material properties was discussed. The results showed that the experimental data fitted well with the Mayers power-law (P = A.dn) and the PSR (P/d = al + a2d) models. The ISE index (n) showed a good correlation with the hardness-elastic modulus ratio (H/E), which potentially could be used to predict the relative contributions of the elastic and plastic deformation contact area under indentation load and to normalize the hardness values for inverse material properties .
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Authors: Xiao Xiang Su, Y.D. Gu, Guo Qing Ruan, Xue Jun Ren
Abstract: This study analyzed the plantar pressure distribution character as the foot position between normal to inversion. Eight healthy male volunteers have participated in the test with the foot position from normal to 20 inversion angles which controlled by wedges. The results of this test showed that the centre of the pressure was clearly transferred from centre to lateral side when the foot position was changed from normal to inversion. In addition, the contact area varied largely between the normal and inversion condition, but changed a little between two inversion loading situations. The finding of this study suggests that the foot injuries could attributed to more of inappropriate foot positioning than the magnitude of loading force.
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Authors: Xiao Xiang Su, Yao Dong Gu, J.P. Finlay, I.D. Jenkinson, Xue Jun Ren
Abstract: Closed cell polymeric foams are widely used in sport and medical equipments. In this study, an artificial neural network (ANN) based inverse finite element (FE) program has been developed and used to predict the nonlinear material properties of EVA foams with multiple layers. A 2-D parametric FE model was developed and validated against experimental data. Systematic data from FE simulations was used to train and validate the ANN model. The accuracy and validity of the ANN method were assessed based on both blind tests and experimental data. Results showed that the proposed artificial neural network model is robust and efficient in predicating the nonlinear parameters of foam materials.
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