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Volume 23, MTME 2025
 
 
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9 pages, 1664 KB  
Proceeding Paper
Integrating Data Science and Numerical Methods for Next-Generation Metal Processing
by Amir M. Horr and Rodrigo Gómez Vázquez
Mater. Proc. 2025, 24(1), 1; https://doi.org/10.3390/materproc2025024001 (registering DOI) - 21 Aug 2025
Viewed by 106
Abstract
The structured integration of analytical methods, numerical simulations, and emerging data science techniques enables a highly efficient and robust modeling approach for manufacturing processes. To successfully implement advanced analytical strategies, numerical methods, and data-driven tools within digital twin or digital shadow frameworks for [...] Read more.
The structured integration of analytical methods, numerical simulations, and emerging data science techniques enables a highly efficient and robust modeling approach for manufacturing processes. To successfully implement advanced analytical strategies, numerical methods, and data-driven tools within digital twin or digital shadow frameworks for next-generation metal processing, several critical requirements must be addressed. This paper discusses the foundational elements necessary for the seamless integration of these technologies, with a focus on achieving impactful optimization and precise control of material processes. The research highlights the outcomes of combining data-driven models with high-fidelity numerical simulations, emphasizing their complementary roles in process control and data generation for future-oriented manufacturing modeling. Full article
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