Work

Artificial neural network-based prediction of functional fatigue behaviour of an NiTi shape memory alloy

Year: 2025

Type: article

Abstract: Shape memory alloys (SMAs), such as NiTi, exhibit phase transformations during cyclic loading, leading to degradation in functional properties like recovery strain and thermal hysteresis, known as functional fatigue. This study proposes an artificial neural network (ANN) approach to model the functional fatigue behaviour of NiTi SMA under partial thermal cycling at constant stress (100 MPa) and varying electrical current (10–17.5 A) across 1000 cycles. A feed-forward backpropagation ANN with two inputs (current, number of cycles) and four outputs (recovery strain, permanent strain, upper cycle temperature, and strain accumulation per cycle) was developed. The ANN achieved a prediction accuracy of 94.3%, indicating its reliability in capturing the complex fatigue response of SMAs.

Source: Discover Materials

DOI: 10.1007/s43939-025-00396-3

Authors: G. Swaminathan, S. H. Adarsh, M. Raju, K. Senthilkumar, T. Senthil Muthu Kumar

Institutions: PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH, Siddaganga Institute of Technology, Easwari Engineering College, Kalasalingam Academy of Research and Education

Language: English

Topic: Shape Memory Alloy Transformations

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,590

OpenAlex ID: W4414947519