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.
Highlights
Additive manufacturing (AM) offers many key benefits, which could change the industrial paradigm in various fields, as a tool-free, cost-efficient and digital approach to manufacturing [1]
Modern data analytics approaches were employed to check the feasibility of predicting melt-pool geometries of powder bed fusion (PBF) single-tracks and the physical relevance to the real phenomena, and the following conclusions were drawn
(1) Machine learning models better predicted melt-pool geometries measured in the substrate with the Pearson’s correlation coefficient (PCC) approach, and those measured in the powder bed with the maximal information coefficient (MIC) approach
Summary
Additive manufacturing (AM) offers many key benefits, which could change the industrial paradigm in various fields, as a tool-free, cost-efficient and digital approach to manufacturing [1]. As well as computational analysis, has been performed to clarify the effects of materials and process parameters on melt-pool characteristics, and the underlying physics [2,3,4,5,6,7]. We introduce an emerging data analytics approach to predict the multi-physics-based phenomena in AM, along with a scientific insight into the underlying mechanisms [10,11,12,13,14,15]. The results demonstrated that the reasonably trained data analytics approach prioritizes key materials/process parameters of the melt-pool formation, with the physical relevance, and facilitates the accurate prediction of melt-pool geometries for the AM process optimization
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