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

Ivanna Baturynska | Zendy; Oleksandr Semeniuta | Zendy; Kristian Martinsen | Zendy

Open Access

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

Author(s) -

Ivanna Baturynska,

Oleksandr Semeniuta,

Kristian Martinsen

Publication year - 2018

Publication title -

procedia cirp

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.683

H-Index - 65

ISSN - 2212-8271

DOI - 10.1016/j.procir.2017.12.204

Subject(s) - finite element method , process (computing) , fusion , quality (philosophy) , product (mathematics) , computer science , engineering drawing , engineering , rapid prototyping , manufacturing engineering , mechanical engineering , process engineering , structural engineering , mathematics , linguistics , philosophy , operating system , geometry , epistemology

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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