Characterization of Shape Memory Alloys Using Artificial Neural Networks

Abstract

Shape memory alloys are capable of delivering advantageous solutions to a wide range of engineering-based problems. Implementation of these solutions, however, is often complicated by the hysteretic, non-linear, thermomechanical behavior of the material. Existing constitutive models are largely capable of accurately describing this unique behavior, but they require prior characterization of material parameters. Current characterization procedures necessitate extensive data collection and data processing, creating a high barrier of entry for shape memory alloy application. This thesis develops a novel approach in which a form of computational intelligence is applied to the task of shape memory alloy material parameter characterization. Specifically, this work develops a methodology in which an artificial neural network is trained to identify transformation temperatures and stress influence coefficients of shape memory alloy specimens using strain-temperature coordinates as inputs. Training data is generated through the use of an existing shape memory alloy constitutive model. Factorial and Taguchi-based methods of generating training data are implemented and compared. Results show that trained artificial neural networks are capable of identifying shape memory alloy material parameters with satisfactory accuracy. Comparison of the implemented training data generation methods indicates that the Taguchi-based approach yields an artificial neural network that outperforms that of the factorial-based approach despite requiring significantly fewer training data specimens.