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Cited by 182Additive Manufacturing Materials and ProcessesAdditive Manufacturing and 3D Printing TechnologiesMachine Learning in Materials Science

Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework

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Baturynska, Ivanna, Semeniuta, Oleksandr, Martinsen, Kristian·2018·Procedia CIRP
DOI 10.1016/j.procir.2017.12.204
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Abstract

In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.

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  • ML and finite element modeling have been used to simulate the physical processes of 3D printing in order to improve the quality of finished products.

    “Novel manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material but may be impossible to produce through traditional metal-casting or poured concrete; ML and finite element modeling have been used to simulate the physical processes of 3D printing in order to improve the quality of finished products [62].”

    From Tackling climate change with machine learning (2023) · cites this paper as [62]

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Research works printed but not linked: 3

  1. Shen, X., Yao, J., Wang, Y. and Yang, J., International Symposium on Neural Networks, Springer; 2004, Density Prediction of Selective Laser Sintering Parts Based on Artificial Neural Network, pp. 832-840.
  2. Ikonen, I., Biles, W. E., Kumar, A., Wissel, J. C. and Ragade, R. K., ICGA, 1997, A Genetic Algorithm for Packing Three-Dimensional Non- Convex Objects Having Cavities and Holes, pp. 591-598.
  3. Ma, L., Fong, J., Lane, B., Moylan, S., Filliben, J., Heckert, A. and Levine, L., International Solid Freeform Fabrication Symposium, Laboratory for Freeform Fabrication and the University of Texas Austin, TX, USA; 2015, Using Design of Experiments in Finite Element Modeling to Identify Critical Variables for Laser Powder Bed Fusion.

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Authors
Baturynska, I., Semeniuta, O., & Martinsen, K.
Affiliations
Norwegian University of Science and Technology (NO)
Corresponding author
Baturynska, Ivanna
ORCID
Venue
Procedia CIRP
Publisher
Elsevier BV
Published
2018
Volume
67
Pages
227-232
ISSN
2212-8271 (ISSN-L)
DOI
10.1016/j.procir.2017.12.204
OpenAlex
W2797690407
Cited by
182
References
32
Citations by year
2026: 12 · 2025: 24 · 2024: 29 · 2023: 23 · 2022: 31 · 2021: 27 · 2020: 17 · 2019: 14 · 2018: 5
FWCI
10.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Procedia CIRP (publishedVersion, cc-by-nc-nd)
  • BIBSYS Brage (BIBSYS (Norway)) (submittedVersion, cc-by-nc-nd)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
Funders
Norges Forskningsråd
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
  • Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
powder bed fusion, additive manufacturing, process parameter optimization, machine learning, finite element analysis, hybrid modeling, part quality, conceptual framework

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