Metallurgical and Materials Data

Data-driven approaches are transforming materials science and metallurgy, enabling deeper insights into complex processes, optimizing production, and improving material performance. By integrating statistical tools and computational methods, researchers and engineers can enhance process efficiency, material design, and predictive capabilities in industrial applications.
The intention of this issue is to analyze the trends in materials statistics and research that utilizes data science, experimental statistics, and computational modeling to advance the understanding and development of materials and metallurgical processes.

Guest Editor,

Aleksandar Jovanović