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Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach

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Abstract

Selective laser melting (SLM) is prevalent as one additive manufacturing (AM) technique to produce metallic components. In this study, a modified GTN (Gurson-Tvergaard-Needleman) model was used to characterize void growth, nucleation, and shear mechanism to predict the ductile fracture behavior of SLM-fabricated Ti6Al4V alloys under uniaxial stress states. An intelligence approach obtained by an artificial neural network (ANN) in conjunction with particle swarm optimization (PSO) algorithms which optimized modified GTN model parameters and found relations between input and output data. In order to assess the accuracy of the results, the PSO suggested parameters were applied to finite element simulation and checked by experimental results in each step; then, the machine was trained by the last data to find desired optimum parameters. The final results showed that the damage and fracture behavior of SLM-fabricated Ti6Al4V alloys under uniaxial stress states are correctly predicted by GTN-modified model and hybrid ANN-PSO optimized parameters.

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The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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The code of this study is available from the corresponding author, upon reasonable request.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

    Majid Shafaie

  2. Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran

    Maziar Khademi

  3. Department of Mechanical, Industrial & Manufacturing Engineering, The University of Toledo, Toledo, OH, USA

    Mohsen Sarparast & Hongyan Zhang

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  1. Majid Shafaie
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  2. Maziar Khademi
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  3. Mohsen Sarparast
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Correspondence to Mohsen Sarparast.

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Shafaie, M., Khademi, M., Sarparast, M. et al. Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach. Int J Adv Manuf Technol 123, 4385–4398 (2022). https://doi.org/10.1007/s00170-022-10522-7

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