Hits:

Indexed by:期刊论文

Date of Publication:2012-09-01

Journal:RARE METAL MATERIALS AND ENGINEERING

Included Journals:SCIE、PKU、ISTIC

Volume:41

Page Number:243-246

ISSN No.:1002-185X

Key Words:shape memory alloy; superelasticity; radial basis function neural network; cyclic constitutive model

Abstract:The mechanical behavior of superelastic shape memory alloy (SMA) under loading and unloading cycles varies gradually and approximates to a steady state ultimately. Based on the cyclic loading tests of superelastic SMA wires, a radial basis function neural network (RBFNN) constitutive model is proposed. In this model, the input includes the number of loading cycles, the index of loading and unloading and the strain; and the output was the stress. Numerical simulations indicate that the model can simulate the cyclic hysteretic behavior of SMA correctly and has a high accuracy of prediction.