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

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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.

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Abbreviations

SME:

Shape memory effect

SE:

Superelasticity

ANN:

Artificial neural network

V :

Scanning speed

P :

Laser power

E v :

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

Y i :

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

f(Ynet):

Nonlinear activation function

Y net :

Summation of weighted inputs

X i :

Neuron input

W i :

Weight coefficient of each neuron input

W 0 :

Bias

J r :

The error between the observed value and network response

O i :

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

R 2 :

Coefficients of determination

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, The University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy

    Mehrshad Mehrpouya

  2. Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184, Rome, Italy

    Annamaria Gisario & Atabak Rahimzadeh

  3. Dynamic and Smart Systems Laboratory, Mechanical Industrial and Manufacturing Engineering Department, The University of Toledo, Toledo, OH, 43606, USA

    Mohammadreza Nematollahi, Keyvan Safaei Baghbaderani & Mohammad Elahinia

Authors
  1. Mehrshad Mehrpouya
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  2. Annamaria Gisario
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  3. Atabak Rahimzadeh
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  4. Mohammadreza Nematollahi
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  5. Keyvan Safaei Baghbaderani
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  6. Mohammad Elahinia
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Correspondence to Mehrshad Mehrpouya.

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Mehrpouya, M., Gisario, A., Rahimzadeh, A. et al. A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy. Int J Adv Manuf Technol 105, 4691–4699 (2019). https://doi.org/10.1007/s00170-019-04596-z

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  • DOI: https://doi.org/10.1007/s00170-019-04596-z

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