首页|Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
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NSTL
Elsevier
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 in-telligence (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 bench-marks. 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.
Scientific data scienceDeep learningAdditive manufacturingPhysics-informed machine learningData-driven discoveryData-driven designNEURAL-NETWORKSPREDICTIVE CONTROLMICROSTRUCTUREOPTIMIZATIONWEARFRAMEWORKCONSENSUSSELECTIONSTEELLAWS
NSTL
Elsevier
