TY - JOUR
T1 - Physics-Informed Machine Learning for metal additive manufacturing
AU - Farrag, Abdelrahman
AU - Yang, Yuxin
AU - Cao, Nieqing
AU - Won, Daehan
AU - Jin, Yu
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Additive manufacturing
KW - Deep Neural Networks
KW - Machine Learning
KW - Physics-Informed Machine Learning
KW - Physics-Informed Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85190434873&partnerID=8YFLogxK
U2 - 10.1007/s40964-024-00612-1
DO - 10.1007/s40964-024-00612-1
M3 - Review article
AN - SCOPUS:85190434873
SN - 2363-9512
JO - Progress in Additive Manufacturing
JF - Progress in Additive Manufacturing
ER -