Sen Liu
Colorado School of Mines
20 Papers
10 Citations
Sen Liu is an academic researcher from Colorado School of Mines. The author has contributed to research in topics: Computer science & Process (computing). The author has an hindex of 4, co-authored 10 publications.
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Papers
Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration
Sen Liu,Branden B. Kappes,Behnam Amin-Ahmadi,Othmane Benafan,Xiaoli Zhang,Aaron P. Stebner,Aaron P. Stebner +6 more
TL;DR: In this paper, a machine learning approach is used to predict shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations.
98
A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing
Rui Liu,Sen Liu,Xiaoli Zhang +2 more
TL;DR: A physics-informed, data-driven model (PIM) is used, which instead of directly using machine setting parameters to predict porosity levels of printed parts, first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure.
98
Machine learning for knowledge transfer across multiple metals additive manufacturing printers
TL;DR: A data-mining-assisted ML knowledge transfer framework is proposed to enable the reuse of previously acquired knowledge and Bayesian models are found to more effectively model process-property relations and outperform support vector machine and logistic regression models.
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In-process comprehensive prediction of bead geometry for laser wire-feed DED system using molten pool sensing data and multi-modality CNN
TL;DR: This paper focuses on using machine learning techniques to enable in-process geometry monitoring by comprehensively modeling the correlation between the real-time molten pool sensing data and bead geometry properties and takes a step towards developing in situ quality control strategy for the metal AM system.
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In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing
TL;DR: In this paper , a convolutional neural network (CNN) model is developed for establishing the correlations directly from in-process molten pool information to the micro-structural properties.
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