Data analytics approach for melt-pool geometries in metal additive manufacturing

Lee, Seulbi; Peng, Jian; Shin, Dongwon; Choi, Yoon Suk

doi:10.1080/14686996.2019.1671140

Data analytics approach for melt-pool geometries in metal additive manufacturing

Journal Article · Wed Oct 16 00:00:00 EDT 2019 · Science and Technology of Advanced Materials

DOI:https://doi.org/10.1080/14686996.2019.1671140· OSTI ID:1648875

Lee, Seulbi ^[1]; Peng, Jian ^[2]; ^[2]; Choi, Yoon Suk ^[1]

Pusan National Univ., Busan (Korea, Republic of)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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 melt-pool 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 of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1648875

Journal Information:: Science and Technology of Advanced Materials, Journal Name: Science and Technology of Advanced Materials Journal Issue: 1 Vol. 20; ISSN 1468-6996

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Data analytics approach for melt-pool geometries in metal additive manufacturing Lee, Seulbi; Peng, Jian; Shin, Dongwon https://doi.org/10.6084/m9.figshare.9988349 The current record is supplemented by this text	text	January 2019
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Data analytics approach for melt-pool geometries in metal additive manufacturing

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