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The International Journal of Advanced Manufacturing Technology

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19-11-2022 | ORIGINAL ARTICLE

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

Authors: Majid Shafaie, Maziar Khademi, Mohsen Sarparast, Hongyan Zhang

Published in: The International Journal of Advanced Manufacturing Technology | Issue 11-12/2022

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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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Title: Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach
Authors: Majid Shafaie
Maziar Khademi
Mohsen Sarparast
Hongyan Zhang
Publication date: 19-11-2022
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 11-12/2022
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-022-10522-7

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