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A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy

  • Mehrshad MehrpouyaEmail author
  • Annamaria Gisario
  • Atabak Rahimzadeh
  • Mohammadreza Nematollahi
  • Keyvan Safaei Baghbaderani
  • Mohammad Elahinia
ORIGINAL ARTICLE
  • 117 Downloads

Abstract

Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated.

Keywords

Additive manufacturing Shape memory alloys NiTi Modeling Artificial neural network 

Nomenclature

SME

Shape memory effect

SE

Superelasticity

ANN

Artificial neural network

V

Scanning speed

P

Laser power

Ev

Energy density

t

Layer thickness

H

Hatch spacing

SLM

Selective laser melting

TT

Transformation temperatures

RR

Recovery ratio

MLP

Multi-layer perceptrons

LM

Levenberg–Marquardt

Yi

The response of the neuron \( \dot{i} \)

f(Ynet)

Nonlinear activation function

Ynet

Summation of weighted inputs

Xi

Neuron input

Wi

Weight coefficient of each neuron input

W0

Bias

Jr

The error between the observed value and network response

Oi

Observed value of the neuron \( \dot{i} \)

R2

Coefficients of determination

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial EngineeringThe University of Roma TreRomeItaly
  2. 2.Department of Mechanical and Aerospace EngineeringSapienza University of RomeRomeItaly
  3. 3.Dynamic and Smart Systems Laboratory, Mechanical Industrial and Manufacturing Engineering DepartmentThe University of ToledoToledoUSA

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