About this Abstract |
Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
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Symposium
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Special Session
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Presentation Title |
Powder Features Affecting Structural and Mechanical Properties of Additively Manufactured Inconel 718: A Machine Learning Analysis |
Author(s) |
Mohammad Shahadath Hossain, Daniel Fernando Silva, Aleksandr Vinel, Jia Liu, Nima Shamsaei |
On-Site Speaker (Planned) |
Mohammad Shahadath Hossain |
Abstract Scope |
The aim of this paper is to select important Inconel 718 powder properties that can have significant effect on the structural and mechanical properties of Laser-Beam Powder Bed Fusion manufactured specimens. The dataset used was provided by NASA and contains powder rheological, morphological, and chemical composition properties. The output variables considered are melt pool depth, high cycle fatigue life, porosity volume fraction and porosity size. Initially, Pearson correlation coefficient matrix is used to reduce the number of predictor features. Several statistical and machine learning algorithms including stepwise regression, LASSO, and random forest regression are used to identify the powder properties that have the strongest impact on the selected outputs. The variables identified using the different statistical and machine learning techniques are similar, which increases the confidence of the findings. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |