When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickne...

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Bibliographic Details
Main Authors: Ke Wang, Minxiang Zeng, Jialu Wang, Wenjie Shang, Yanliang Zhang, Tengfei Luo, Alexander W. Dowling
Format: Article
Language:English
Published: Elsevier 2023-03-01
Series:Digital Chemical Engineering
Subjects:
Aerosol jet printing
Model-based design of experiment
Data science
Uncertainty quantification
Gaussian process regression
Online Access:http://www.sciencedirect.com/science/article/pii/S2772508122000679

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http://www.sciencedirect.com/science/article/pii/S2772508122000679

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