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Advantages of Hybrid Neural Network Architectures to Enhance Prediction of Tensile Properties in Laser Powder Bed Fusion

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Abstract:

The properties of AlSi10Mg produced by Laser Powder Bed Fusion (PBF-LB) are defined by a multitude of different machine and laser parameters. This multi-parameter space presents the challenge of optimizing the material properties for a given application by the sheer amount of possible parameter combinations. Characterizing this multi-parameter space empirically is limited by time and resources and thus yields an incomplete picture of the process capabilities and local optima, respectively. To improve on this situation, machine learning to map the process parameters on the tensile properties of AlSi10Mg was used. The Hybrid Neural Network (HNN) used in this study consisted of a Convolutional Neural Network (CNN) to process the micrographs and a Dense Neural Network (DNN) to process the LPBF process parameters as well as the output of the CNN. The micrographs given to the CNN part of the network were printed with the same parameters given to the DNN part to include the information of the bulk microstructure as it strongly influences the tensile properties of the material. With the HNN, we observed good accuracy of the predicted tensile properties, given the small amount of training data. Furthermore, we explore which features of the micrographs were extracted by the CNN.

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Periodical:

Key Engineering Materials (Volume 964)

Pages:

65-71

DOI:

https://doi.org/10.4028/p-0tCaMF

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Online since:

November 2023

Authors:

Florian Funcke*, Tobias Forster, Peter Mayr

Keywords:

Additive Manufacturing, Laser Powder Bed Fusion, Micrographs, Neural Networks, Thermec 2023

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© 2023 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] K. Riener et al.: Influence of particle size distribution and morphology on the properties of the powder feedstock as well as of AlSi10Mg parts produced by laser powder bed fusion (LPBF), Additive Manufacturing 34 (2020)

DOI: 10.1016/j.addma.2020.101286

Google Scholar

[2] Meng, L. et al. Machine learning in additive manufacturing: a review. JOM 72, 2363–2377 (2020)

Google Scholar

[3] Jiang, M., Mukherjee, T., Du, Y. & DebRoy, T. Superior printed parts using history and augmented machine learning. npj Computational Materials 8, 184 (2022)

DOI: 10.1038/s41524-022-00866-9

Google Scholar

[4] Liu, Q. et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms. Acta Materialia 201, 316–328 (2020).

DOI: 10.1016/j.actamat.2020.10.010

Google Scholar

[5] Zuiderveld, K., VIII.5. - Contrast Limited Adaptive Histogram Equalization, in: P. S. Heckbert, (ed.), Graphics Gems, Academic Press, 1994, p.474–485

DOI: 10.1016/b978-0-12-336156-1.50061-6

Google Scholar

[6] G. Bradski, The OpenCV Library, Dr Dobb's Journal of Software Tools, (2000)

Google Scholar

[7] Patel, S. & Vlasea, M. Melting modes in laser powder bed fusion. Materialia 9, 100591(2020).

DOI: 10.1016/j.mtla.2020.100591

Google Scholar

[8] A. Paszke,et al., Pytorch: An imperative style, high-performance deep learning library, in: H. Wallach et al. (eds.), Advances in neural information processing systems 32, 2019, pp.8024-8035

Google Scholar

[9] Johnson et al.: Invited review: Machine Learning for materials development in metals additive manufacturing, Additive Manufacturing 36 (2020)

DOI: 10.1016/j.addma.2020.101641

Google Scholar

[10] Rahaman et al. : Machine Learning to Predict the Martensite Start Temperature in Steels, Metallurgical and Materials Transactions A, (2019)

Google Scholar