Potentials - July/August 2016 - 31
is now possible to remotely visual-
ize, analyze, optimize, and control
the manufacturing process.
The "factory of the future" as a
concept has been around for several
decades, but the technologies for fully
enabling it at a low cost (for wide pene-
tration) have evolved only recently. The
promise of increasing the utilization
efficiency of manufacturing resourc-
es (especially materials and energy),
coupled with reducing the emission of
hazardous substances (into ground,
water, and air) has provided addition-
al momentum to the wave of smart
manufacturing sweeping across the
world. Furthermore, smart technolo-
gies allow distributed manufacturing,
leading to smaller-scale remote pro-
duction closer to end-users, enabling
wider employment opportunities as
well as better understanding and ful-
filment of user needs.
There are five key technology driv-
ers for smart manufacturing: vir-
tual engineering, Cloud computing,
smart sensors, the Internet of Things
(IoT), and big data analytics. Virtual
engineering refers to computer-aided
design (CAD), analysis, simulation,
testing, and optimization of prod-
uct design, tooling, and the manu-
facturing process. Cloud computing
(essentially a network of computers
with preinstalled software that can
be accessed as service over the In-
ternet) enables distribution of above
tasks by teams working in different
locations, who share common data-
bases. Smart sensors embedded in
process equipment collect data such
as temperature, pressure, vibration,
and noise; other sensors (such as
cameras and radio frequency identi-
fication) track the movement of ma-
terials and in-process parts through
the factory.
The IoT refers to the network
of software, databases, sensors,
and other objects, enabling the ex-
change of data and the integration
of virtual and physical worlds. Big
data (by definition, data sets that
cannot be handled by traditional
methods) streamed by a number
of sensors and stored in the Cloud,
can be analyzed by specially devel-
oped software programs to detect
patterns that can be used to pre-
dict and prevent potential problems
in manufacturing processes.
All five technology drivers for
smart manufacturing have one thing
in common-they are part of the
broad set of information technologies,
where India has made rapid progress
in spite of its many challenges, in-
cluding extreme poverty and ethnic
diversity. The country has crossed
one billion registered mobile phone
users. Its information technology
(IT) and IT-enabled services indus-
tries employ over 10 million people
(mostly young) and generate reven-
ues worth US$150 billion. Is it possi-
ble to leverage this resource to drive
smart manufacturing, especially in
metal casting?
sMaRt foundry
The challenge was taken up by ten
technical institutes (public as well
as private) and research institutes
in India, who joined hands to "rein-
vent" the metal casting process for
education and entrepreneurship, by
leveraging the relevant enabling
technologies. The acronym SMART
stands for sustainable metalcasting
by advanced research and technol-
ogy. Its goal is to develop a compact
SMART foundry that can be used
for rapid manufacture of small
parts required in tiny order quanti-
ties, which is not economical for
conventional foundries. A large
number of such parts are required
Part CAD
for prototyping purposes and main-
taining old but valuable machinery.
The entire facility can be set up in
a small room (25 m2), which is ideal
for training students, who, in turn,
can set up micromanufacturing
units with very little investment.
They can also use it for making metal
busts of people, household applianc-
es, hobby work, and other innovative
applications. The new process aims
to change the perception of metal
casting from "dirty, difficult, and
dangerous" to "sustainable, smart,
and safe."
The architecture of the overall sys-
tem is shown in Fig. 3. It will have the
following key technology elements.
■■Intelligent design: Part models
are created using 3-D CAD or
3-D scanning and converted into
pattern models by providing draft
or taper (for their easy removal
from mold), fillets (for smooth
flow of metal), and allowance for
shrinkage (during solid-state
cooling of metal), as well as
machining. Then gating channels
(through which liquid metal
poured from a ladle will flow into
part cavity), and feeders (to com-
pensate for volumetric shrinkage
during phase change) are mod-
eled to obtain the 3-D model of
full casting. This is verified by
the simulation of molding filling
and casting solidification to
achieve the desired quality at
maximum possible yield.
Tooling
Design
Methods
Design
Process
Simulation
Design
3-D Pattern
Printer
Automatic
Modeling
Melting +
Pouring
Manufacture
Build Status,
Material,
Accuracy
Mold, Mixture,
Moisture,
Weight
Weight,
Power,
Temperature
Monitoring
Foundary Data Analytics Engine
Data
Material
Visualization
(Dashboard)
Evaluation
(Quality, Time)
Optimization
(Process Par.)
Analytics
Fig3 the building blocks of a Smart foundry system.
IEEE PotEntIals
Jul y/August 201 6
■
31
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