Data analytics approach for melt-pool geometries in metal additive manufacturing. Issue 1 (31st December 2019)

• Record Type: Journal Article
• Authors: Lee, Seulbi; Peng, Jian; Shin, Dongwon; Choi, Yoon Suk
• Is Part Of: Science and technology of advanced materials. Volume 20:Issue 1(2019)
• Page Start: 972
• Page End: 978
• Publication Date: 2019-12-31
• Subjects: Powder bed fusion (PBF) process -- melt-pool -- single track -- machine learning -- correlation analysis; 106 Metallic materials -- 404 Materials informatics / Genomics; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2019.1671140
• Languages: English
• ISSNs: 1468-6996

Abstract

ABSTRACT: 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. Abstract : uf0001