TY - JOUR
T1 - Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes
T2 - Current state and perspectives
AU - Mozaffar, Mojtaba
AU - Liao, Shuheng
AU - Xie, Xiaoyu
AU - Saha, Sourav
AU - Park, Chanwook
AU - Cao, Jian
AU - Liu, Wing Kam
AU - Gan, Zhengtao
N1 - Funding Information:
This work was supported by the Vannevar Bush Faculty Fellowship N00014-19-1-2642, National Institute of Standards and Technology (NIST) - Center for Hierarchical Material Design (CHiMaD) under grant No. 70NANB14H012 , and the National Science Foundation (NSF) under grants No. CPS/CMMI-1646592 and CMMI-1934367 .
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/4
Y1 - 2022/4
N2 - Today's manufacturing processes are pushed to their limits to generate products with ever-increasing quality at low costs. A prominent hurdle on this path arises from the multiscale, multiphysics, dynamic, and stochastic nature of many manufacturing systems, which motivated many innovations at the intersection of artificial intelligence (AI), data analytics, and manufacturing sciences. This study reviews recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights. Mechanistic-AI solutions are systematically analyzed for three aspects of manufacturing processes, i.e., modeling, design, and control, with a focus on approaches that can improve data requirements, generalizability, explainability, and capability to handle challenging and heterogeneous manufacturing data. Additionally, we introduce a corpus of cutting-edge Mechanistic-AI methods that have shown to be very promising in other scientific fields but yet to be applied in manufacturing. Finally, gaps in the knowledge and under-explored research directions are identified, such as lack of incorporating manufacturing constraints into AI methods, lack of uncertainty analysis, and limited reproducibility and established benchmarks. This paper shows the immense potential of the Mechanistic-AI to address new problems in manufacturing systems and is expected to drive further advancements in manufacturing and related fields.
AB - Today's manufacturing processes are pushed to their limits to generate products with ever-increasing quality at low costs. A prominent hurdle on this path arises from the multiscale, multiphysics, dynamic, and stochastic nature of many manufacturing systems, which motivated many innovations at the intersection of artificial intelligence (AI), data analytics, and manufacturing sciences. This study reviews recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights. Mechanistic-AI solutions are systematically analyzed for three aspects of manufacturing processes, i.e., modeling, design, and control, with a focus on approaches that can improve data requirements, generalizability, explainability, and capability to handle challenging and heterogeneous manufacturing data. Additionally, we introduce a corpus of cutting-edge Mechanistic-AI methods that have shown to be very promising in other scientific fields but yet to be applied in manufacturing. Finally, gaps in the knowledge and under-explored research directions are identified, such as lack of incorporating manufacturing constraints into AI methods, lack of uncertainty analysis, and limited reproducibility and established benchmarks. This paper shows the immense potential of the Mechanistic-AI to address new problems in manufacturing systems and is expected to drive further advancements in manufacturing and related fields.
KW - Additive manufacturing
KW - Data-driven design
KW - Data-driven discovery
KW - Deep learning
KW - Physics-informed machine learning
KW - Scientific data science
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U2 - 10.1016/j.jmatprotec.2021.117485
DO - 10.1016/j.jmatprotec.2021.117485
M3 - Review article
AN - SCOPUS:85122618505
SN - 0924-0136
VL - 302
JO - Journal of Materials Processing Technology
JF - Journal of Materials Processing Technology
M1 - 117485
ER -