Data-driven surrogate modelling of residual stresses in Laser Powder-Bed Fusion

Author:

Lestandi L.1,Wong J.C.2,Dong G.Y.3,Kuehsamy S. J.2,Mikula J.2,Vastola G.2,Kizhakkinan U.4,Ford C.S.2,Rosen D.W.25,Dao M.H.2,Jhon M.H.2

Affiliation:

1. Nantes Université, École Centrale Nantes, CNRS, Institut de Recherche en Génie Civil et Mécanique (GeM), Nantes, France

2. Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore

3. Department of Mechanical Engineering, University of Colorado Denver, Denver, USA

4. Digital Manufacturing and Design Center, Singapore University of Technology and Design, Singapore

5. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Funder

Science and Engineering Research Council

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering

Reference66 articles.

1. Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S.Moore, D. G. Murray, B. Steiner, P.Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu. 2016. “TensorFlow: A System for Large-Scale Machine Learning.” Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016. https://doi.org/10.5555/3026877.3026899.

2. Parametric solutions involving geometry: A step towards efficient shape optimization

3. Mechanical Properties of 3D-Printed Maraging Steel Induced by Environmental Exposure

4. Black box approximation of tensors in hierarchical Tucker format

5. A POD-selective inverse distance weighting method for fast parametrized shape morphing

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1. Special issue on machine learning in additive manufacturing;International Journal of Computer Integrated Manufacturing;2023-07-11

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