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

Autor:	Seulbi Lee, Jian Peng, Dongwon Shin, Yoon Suk Choi
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	powder bed fusion (pbf) process melt-pool single track machine learning correlation analysis Materials of engineering and construction. Mechanics of materials TA401-492 Biotechnology TP248.13-248.65
Zdroj:	Science and Technology of Advanced Materials, Vol 20, Iss 1, Pp 972-978 (2019)
Druh dokumentu:	article
ISSN:	1468-6996 1878-5514 14686996
DOI:	10.1080/14686996.2019.1671140
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4b55fb3da9c44cd4aae5710e31b83774 Zobrazit plný text záznamu View record in DOAJ