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Visualization of Steel Continuous Casting Including a New Integral Method for Post‐Processing Temperature Data
Author(s) -
Zappulla Matthew L. S.,
Cho SeongMook,
Thomas Brian G.
Publication year - 2019
Publication title -
steel research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 49
eISSN - 1869-344X
pISSN - 1611-3683
DOI - 10.1002/srin.201800540
Subject(s) - visualization , computer science , process (computing) , field (mathematics) , creative visualization , continuous casting , scalar (mathematics) , engineering drawing , data mining , engineering , materials science , mathematics , geometry , composite material , pure mathematics , operating system
Understanding and improving complex manufacturing processes such as steel continuous casting can have a tremendous global impact. Due to the extremely harsh environments, computational modeling is more important than ever in the study of these processes. Being able to visualize the model results in a manner that conveys accurate process understanding as well as being visually digestible can be difficult, especially for transient, three‐dimensional models. Depending on the objective, it is also important to distinguish between graphically‐pleasing and physically‐based visualization. Following physically‐based visualization guidelines can help make the model results easier to understand and interpret. Examples include: color maps based on temperature, glyphs to show multiple stress components, vector plots overlaying a scalar field, diverging and discrete color maps to differentiate tension and compression, and orienting figures so that gravity acts downwards. This paper explores methodologies for post processing, as well as the selection of some visualization parameters, for a variety of variables and models relevant to understanding and quantifying the complex phenomena in steel continuous casting. Finally a methodology is introduced for the processing of high‐density data from temperature sensors to better visualize potential quality issues on the surface of a continuously cast slab.