Web: http://arxiv.org/abs/2201.11662

Jan. 28, 2022, 2:11 a.m. | Parand Akbari, Francis Ogoke, Ning-Yu Kao, Kazem Meidani, Chun-Yu Yeh, William Lee, Amir Barati Farimani

cs.LG updates on arXiv.org arxiv.org

Characterizing meltpool shape and geometry is essential in metal Additive
Manufacturing (MAM) to control the printing process and avoid defects.
Predicting meltpool flaws based on process parameters and powder material is
difficult due to the complex nature of MAM process. Machine learning (ML)
techniques can be useful in connecting process parameters to the type of flaws
in the meltpool. In this work, we introduced a comprehensive framework for
benchmarking ML for melt pool characterization. An extensive experimental
dataset has been …

arxiv learning machine machine learning manufacturing melt prediction

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