Data analytics approach for melt-pool geometries in metal additive manufacturing / Seulbi Lee ...[et.al.].
Call Number: Repr.M46 Material type:
ArticleSubject(s): Powder bed fusion process | Melt-poo | Single track | Machine learning | Correlation analysis
In:
20 (1)2019:972-978 Science and Technology of Advanced MaterialsSummary: Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of meltpool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction ofmelt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.
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YJ2019 M11
Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of meltpool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction ofmelt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.
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