International Journal on Interactive Design and Manufacturing

Artificial neural network for performance modelling of shape memory alloy

Sivaraos ¹

Rakesh Kumar Phanden ^{2, 3, 4}

K Y Sara Lee ⁵

E J Abdullah ⁶

K. Kumaran ⁷

A S M Al-Obaidi ⁸

R. Devarajan ⁹

Hide authors affiliations

Faculty of Industrial and Manufacturing Engineering & Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

Department of Mechanical Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India |

Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, Australia |

⁴

Sat Kabir Institute of Technology & Management, VPO Ladrawan, Bahadurgarh, India

⁵

Department of Mechanical Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia

⁶

Department of Aerospace Engineering, Universiti Putra Malaysia, Serdang, Malaysia |

⁷

Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia |

⁸

Faculty of Innovation and Technology School of Engineering, Taylor’s University, Selangor, Malaysia

⁹

Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia |

Publication type: Journal Article

Publication date: 2025-02-13

Springer Nature

Journal: International Journal on Interactive Design and Manufacturing

scimago Q2

SJR: 0.394

CiteScore: 4.0

Impact factor: 2.1

ISSN: 19552513, 19552505

DOI: 10.1007/s12008-025-02249-y

Copy DOI

Abstract

In recent years, significant strides in technological advancement have revolutionized our lifestyles, driving a surge in demand for multifunctional and intelligent materials. Among these materials, Shape Memory Alloy (SMA) stands out for its unique ability to memorize and revert to its original shape through phase transformation. Despite its remarkable properties, SMAs exhibit a minor limitation in accurately retaining their original shape or length. Furthermore, there is a notable dearth of information regarding the modelling of SMA behaviour with high precision. This study endeavors to address these challenges by integrating experimental data with neural network modelling to comprehensively examine SMA behaviour for mechanical applications. Leveraging an experimental dataset, this research employs feedforward backpropagation neural network (BPNN) modelling to forecast the strain recovery of SMA Nitinol alloy. The model aims to predict the recovery strain of SMA by utilizing three input parameters: temperature conditional, number of coils, and initial length. Remarkably, the achieved error rates of 0.29%, 0.80%, and 9.20% for various strain measurements significantly undercut the commonly accepted error threshold of 10% for nonlinear data predictions in SMA behaviour. The final results underscore the high prediction accuracy of the Artificial Neural Network (ANN), offering promising prospects for SMA applications involving temperature-strain interactions and enhancing engineering design.