Thomas is professor of mechanical engineering at the University
of Illinois at Urbana-Champaign and was advisor to JOM
from the Process Modeling, Analysis and Control Committee of the Extraction
and Processing, and the Materials Processing and Manufacturing Divisions
Exploring traditional, innovative, and revolutionary issues in the minerals,
metals, and materials fields.
Brian G. Thomas
Modeling and Simulation: Commentary
Casting Process Simulation and Visualization: A JOM-e Perspective
the increasing power of computer hardware and software, computational simulation
and visualization are becoming increasingly important tools to understand and
improve industrial processes, such as metal casting. Computer-aided visualization
is increasing the power of all of the tools available to the solidification
process engineer, including previous literature, mathematical modeling, laboratory
experimentation, and online measurement of the casting processes. This article
exemplifies all four of these.
This article is possible only through the format of on-line publication. The
article features six separate contributions, published by TMS in the electronic
portion of this journal: JOM-e,
Each contribution provides several state-of-the-art computer animations and
explains how these visualizations are being used to increase understanding and
to improve a wide range of different casting processes. This relatively new
format of archival publication allows anyone with an Internet browser to search
and read the articles and watch the animations, all at no cost. Video publication
was pioneered in 1996 with the Wiley journal, Visualization of Engineering Research,
edited by V. Voller. Online publication in JOM-e
and elsewhere now provides researchers a powerful medium to showcase their results
through animation, and to thereby communicate their knowledge faster and better
than ever before.
The first four articles feature advanced computational simulations of transient
solidification phenomena. As solidification is inherently a transient process,
it should not be surprising that our understanding of the modeling results is
greatly aided by animations. A diverse range of casting process phenomena are
simulated in these articles, starting with fluid flow during the filling of
foundry castings in The
Mold Filling and Solidification of a Complex Foundry Casting, by S.G.R.
Brown et al. Also reviewed is transient flow during the continuous casting of
aluminum, in The
Multiphysics Modeling of Solidification and Melting Processes, by
M. Cross et al., and The
Numerical Simulation of Continuous and Investment Casting, by P. Thévoz
et al.; steel (Thévoz et al., and Transient
Fluid Flow in a Continuous Steel-Slab Casting Mold, by Thomas et al.);
magnesium (Thévoz et al.) and copper (Thévoz et al.). Other casting
process simulations include horizontal and twin-roll continuous casting and
the directional solidification of turbine blades (Thévoz et al.). Most
of the simulations also feature coupling with the accompanying evolution of
solidification phenomena, temperature, grain structure, even stress fields.
Simulations even illustrate the coupling of fluid flow with electromagnetic
forces and solidification in cold-crucible melting and levitated droplet processes
(Cross et al.). Models have been further extended to predict important microstructural
properties, such as dendrite arm spacing (Brown et al.) and grain size distributions
(Thévoz et al.) and defects such as porosity (Thévoz et al.),
cold shuts (Cross et al.), and hot cracking sensitivity (Thévoz et al.).
As shown in these contributions, mathematical models of casting processes are
now tackling multiphysics problems of staggering complexity. This sophistication
is necessary to contribute to understanding and solving the complex problems
that affect real processes. This modeling revolution is possible because computer
speed and memory have both increased many orders of magnitude in recent years,
while simultaneously, hardware costs have dramatically decreased. As computing
power continues to improve, advanced computational simulations will play an
increasing role in process development, often replacing traditional experiments.
Computer advances for laboratory experiments now enable the rapid capture of
digital images and their processing into powerful visualizations. One contribution
(Thomas et al.) compares transient computations with animated velocity vectors
from digital particle image velocimetry measurements of fluid flow in a 1m high
water model of the continuous-casting process for steel slabs. Creating such
videos used to take weeks, but now can be viewed in real time. Another contribution,
Observation of Coalescence and Hot Tear Formation in Organic Alloys
by P.-D. Grasso et al., shows close-up slowmotion videos of hot tearing proceeding
between individual dendrites, while subjected to tensile loading during solidification.
These powerful advances in computer-assisted measurement and visualization are
enabling more insights to be derived from laboratory experiments of any size.
Finally, computer advances now enable real-time, on-line measurement, visualization,
and control of real casting processes. The final contribution, Analyzing
Casting Problems by the On-line Monitoring of Continuous Casting Mold Temperatures,
P. Hemy et al., shows evolving temperature contours in the mold of the thin
slab caster at Algoma Steel, which were calculated during casting from the on-line
measurements of thermocouples embedded in the funnel mold. Several different
sample visualizations are provided (Hemy et al.), which illustrate how the color
temperature maps evolve during upsets in the casting process, such as longitudinal
cracks, meniscus tears, SEN ruptures, loss of taper, and breakouts. Videos such
as these, together with other data, enable the process engineer to perform forensic
metallurgy to understand how defects evolve. By recognizing characteristic
patterns in the signals, impending problems can be diagnosed before they happen.
Armed with this understanding, casting operators can monitor the video images
in real time during casting, and take appropriate preventative action, such
as lowering the casting speed. Finally, the encoding of this knowledge into
computer logic allows active on-line computer control of the casting process
parameters to anticipate and avoid defects during the process.
In summary, computer advances are enabling better process model simulations,
laboratory experiments, and communication of the results. As process understanding
increases, and both computer monitoring and control capabilities improve, advanced
on-line control methods will become commonplace in achieving more consistent
quality from materials processes. Computer visualizations are improving all
of the tools of the process engineer to help make this happen.
For more information, contact B.G. Thomas, University of Illinois,
Department of Mechanical & Industrial Engineering, 1206 West Green Street,
Urbana, IL 61801; (217) 333-6919; fax (217) 244-6543; e-mail email@example.com.
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