Physics-Informed Machine Learning for metal additive manufacturing

The advancement of additive manufacturing (AM) technologies has facilitated the design and fabrication of innovative and complicated structures or parts that cannot be fabricated with traditional subtractive manufacturing processes. To achieve the desired functional performance of a specific part, q...

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Bibliographic Details
Published in:Progress in additive manufacturing 2024-04, Vol.10 (1), p.171-185
Main Authors: Farrag, Abdelrahman, Yang, Yuxin, Cao, Nieqing, Won, Daehan, Jin, Yu
Format: Article
Language:English
Subjects:
Engineering
Lasers
Machines
Manufacturing
Materials Science
Optical Devices
Optics
Photonics
Processes
Review Article
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Summary:The advancement of additive manufacturing (AM) technologies has facilitated the design and fabrication of innovative and complicated structures or parts that cannot be fabricated with traditional subtractive manufacturing processes. To achieve the desired functional performance of a specific part, quality and process should be well monitored, controlled, and optimized with advanced modeling techniques. Despite the effectiveness of existing physics-based and data-driven methods, they have limitations in providing generalizability, interpretability, and accuracy for complex metal AM process optimization and prediction solutions. This work emphasizes Physics-Informed Machine Learning (PIML) as a significant recent development, embedding physics knowledge (e.g., thermomechanical laws and constraints) into Machine Learning (ML) models to ensure their reliability and interpretability, as well as enhancing model predictive accuracy and efficiency while addressing the limitations of traditional approaches. The paper further classifies PIML into three categories, emphasizing physics integration in terms of Physics-Informed Domain Knowledge, Simulation-Based Input Data, and Physics-Guided Model Training. In this context, the Physics-Informed Neural Network (PINN) serves as a notable example of Physics-Guided Model Training. PINN is particularly noteworthy for its ability to yield more explainable and reliable results in forward problem solving, even with noisy training data. In addition, the paper further discusses the limitations and potential solutions of PINN.
ISSN:2363-9512
2363-9520
DOI:10.1007/s40964-024-00612-1