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


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.


Additive manufacturing Shape memory alloys NiTi Modeling Artificial neural network 



Shape memory effect




Artificial neural network


Scanning speed


Laser power


Energy density


Layer thickness


Hatch spacing


Selective laser melting


Transformation temperatures


Recovery ratio


Multi-layer perceptrons




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


Nonlinear activation function


Summation of weighted inputs


Neuron input


Weight coefficient of each neuron input




The error between the observed value and network response


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


Coefficients of determination



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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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