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    A transfer learning-based machine learning approach to predict mechanical properties of different material types fabricated by selective laser melting process
    (Sage Publications Ltd, 2023) Pashmforoush, Farzad; Seyedzavvar, Mirsadegh
    The necessity for a massive dataset has limited the desirability of the machine learning approaches for industrial applications, especially in the metal additive manufacturing processes, where, collecting a large dataset is expensive and virtually ineffective. Concerning this restriction, an effective machine learning technique should be developed to bridge the gap between the academia and the industry. Hence, in this research, a transfer learning-based artificial neural network (TL-ANN) model was developed to predict the mechanical properties of different metallic specimens fabricated by selective laser melting (SLM) process. This model was integrated with a Bayesian hyperparameters optimization algorithm to select the optimum training parameters of the model. The proposed model consists of a target network and a source network. The source network was trained based on the mechanical properties that were obtained experimentally for various materials, including pure and alloyed copper, steel, titanium, nickel, etc. The overall regression correlation coefficient (R) of the TL-ANN model was about 0.99, with the mean square error of testing, validation, and training of datasets of about 2.031, 1.423, and 1.068, respectively, representing the successful execution of the source network in prediction of the mechanical properties of the SLMed parts. Using the achieved knowledge of the source network, the target network was trained to predict the mechanical properties of the target material (here SLMed pure and alloyed aluminum specimens). The obtained results revealed that with the help of the transfer learning, the hybrid neural network could predict the mechanical properties of SLM-fabricated aluminum parts with a high accuracy level, even with the small number of training dataset of the target material. To demonstrate the influence of transfer learning in the accuracy of the model, a separate network was developed from scratch, i.e. with random initial weights of the neurons. The R-values of the test dataset of the individual model for the output parameters of ultimate tensile strength, relative density, and yield strength of the fabricated aluminum samples were 0.787, 0.742, and 0.817, respectively, as compared with that of TL-ANN model of 0.966, 0.903, and 0.971, respectively, representing an average of 21% enhancement in the accuracy of the predictivity of the model by application of transfer learning algorithm.
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    Öğe
    Molecular dynamic approach to predict thermo-mechanical properties of poly(butylene terephthalate)/CaCO3 nanocomposites
    (Elsevier Ltd, 2021) Seyedzavvar, Mirsadegh; Boğa, Cem; Akar, Samet; Pashmforoush, Farzad
    Thermo-mechanical properties of poly(butylene terephthalate) polymer reinforced with carbonate calcium nanoparticles have been investigated using molecular dynamics simulations. Detailed analyses have been conducted on the effects of nanofiller content, at concentration levels of 0–7 wt%, on the mechanical properties of PBT, i.e. Young's modulus, Poisson's ratio and shear modulus. Thermal properties, including thermal conductivity and glass transition temperature, have been determined using Perl scripts developed based on nonequilibrium molecular dynamics and a high temperature annealing procedure, respectively. Experiments have been performed to verify the accuracy of the results of MD simulations. The CaCO3/PBT nanocomposites were synthesized using melt blending and mold injection techniques. The uniaxial tensile test, thermal conductivity, differential scanning calorimetry and x-ray diffraction spectroscopy measurements were conducted to quantify the thermo-mechanical properties of such nanocomposites experimentally. The results showed significant improvements in the mechanical properties by addition of CaCO3 nanoparticles due to strong binding between rigid particles and PBT polymer and high nucleation effects of nanoparticles on the matrix. Thermal conductivity and glass transition temperature of nanocomposites represented a consistent increase with the ratio of CaCO3 nanoparticles up to 5 wt% with an enhancement of 38% and 36% with respect to that of pure PBT, respectively. © 2021 Elsevier Ltd