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Sen Liu
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A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing
R Liu, S Liu, X Zhang
The International Journal of Advanced Manufacturing Technology 113 (7), 1943 …, 2021
902021
Physics-informed machine learning for composition –process –property design: Shape memory alloy demonstration
S Liu, BB Kappes, B Amin-ahmadi, O Benafan, X Zhang, AP Stebner
Applied Materials Today 22, doi.org/10.1016/j.apmt.2020.100898, 2020
852020
Machine learning for knowledge transfer across multiple metals additive manufacturing printers
S Liu, AP Stebner, BB Kappes, X Zhang
Additive Manufacturing 39, 101877, 2021
632021
In-process comprehensive prediction of bead geometry for laser wire-feed DED system using molten pool sensing data and multi-modality CNN
ND Jamnikar, S Liu, C Brice, X Zhang
The International Journal of Advanced Manufacturing Technology 121 (1), 903-917, 2022
132022
In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing
ND Jamnikar, S Liu, C Brice, X Zhang
Journal of Manufacturing Processes 79, 803-814, 2022
112022
Simulation of particle trajectory in the head–disk interface
S Liu, H Li, S Shen, S Wu
IEEE Transactions on Magnetics 51 (11), 1-4, 2015
112015
Simulation of particle rebounding from the slider air bearing surface
S Liu, H Li, S Shen, S Wu
Microsystem Technologies 22, 1475-1481, 2016
82016
Hierarchical bead materials multi-property design for wire-feed laser additive manufacturing
S Liu, C Brice, X Zhang
Journal of Manufacturing Processes 80, 546-557, 2022
62022
Interrelated process-geometry-microstructure relationships for wire-feed laser additive manufacturing
S Liu, C Brice, X Zhang
Materials Today Communications 31, 103794, 2022
62022
Simulation of air flow and particle trajectories in the head–disk interface
F Cui, H Li, S Shen, S Liu, S Wu
IEEE Transactions on Magnetics 52 (12), 1-5, 2016
52016
Comprehensive Quality Investigations of Wirefeed Additive Manufacturing based on Machine Learning of Experimental Data
S Liu, C Brice, X Zhang
arXiv preprint arXiv 2103, 2021
42021
A physics-informed feature engineering approach to use machine learning with limited amounts of data for alloy design: shape memory alloy demonstration
S Liu, BB Kappes, B Amin-ahmadi, O Benafan, AP Stebner, X Zhang
CoRR, 1-32, 2020
42020
Modeling of formation and breaking of lubricant bridge in the head–disk interface by molecular dynamic simulation
X Dai, H Li, X Lei, S Shen, S Wu, S Liu, H Du
Molecular Simulation 44 (2), 94-99, 2018
42018
Simulations of particle trajectories in hard disk drives considering the trapping criterion
G Zhang, Y Zhu, H Li, S Shen, Y Yang, S Liu, X Lei, S Wu
IEEE Transactions on Magnetics 52 (8), 1-6, 2016
42016
Comprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing
N Jamnikar, S Liu, C Brice, X Zhang
arXiv preprint arXiv:2103.11588, 2021
32021
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration
S Liu, BB Kappes, B Amin-ahmadi, O Benafan, X Zhang, AP Stebner
arXiv preprint arXiv:2003.01878, 2020
32020
Comprehensive molten pool condition-process relations modeling using CNN for wire-feed laser additive manufacturing
N Jamnikar, S Liu, C Brice, X Zhang
Journal of Manufacturing Processes 98, 42-53, 2023
22023
Automated phase segmentation and quantification of high-resolution TEM image for alloy design
S Liu, B Amin-Ahmadi, R Liu, Q Zheng, X Zhang
Materials Characterization 199, 112779, 2023
22023
Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data
N Jamnikar, S Liu, C Brice, X Zhang
arXiv preprint arXiv:2103.12066, 2021
22021
Numerical Study of Particle Rebound in the Head–Disk Interface
F Cui, H Li, S Shen, S Liu, Y Huang, L Chen, S Wu
IEEE Transactions on Magnetics 54 (11), 1-5, 2018
22018
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