TY  - SER
AU  - Lee,Seulbi
AU  - Peng,Jian
AU  - Shin,Dongwon
AU  - Choi,Yoon Suk
TI  - Data analytics approach for melt-pool geometries in metal additive manufacturing
KW  - Powder bed fusion process
KW  - Melt-poo
KW  - Single track
KW  - Machine learning
KW  - Correlation analysis
N1  - สนใจติดต่อขอรับบริการเอกสารฉบับเต็มที่ one stop service (สำนักหอสมุดฯ) หรือ e-mail (info@dss.go.th) โทร. 0 2201 7254-56, 0 2201 7287-89 (จัดเก็บชั้น 5 - Repr.M46); YJ2019 M11
N2  - 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
ISSN  - 1468-6996
Sources  - 20 (1)2019:972-978
Sources  - Science and Technology of Advanced Materials
ER  -