Integrating Data Science and Numerical Methods for Next-Generation Metal Processing

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.