Digital Twin + Industrial Internet for Smart Manufacturing: A Case Study in the Steel Industry
accessibility, availability and elasticity at low
cost through economies of scale.
Furthermore, these technologies have
matured, making it feasible deploy in small
datacenters and small clusters of servers to
enable small-scale distributed computing on
the
edge
in
the
manufacturing
environment—with
the
benefits
of
scalability, reliability and ease of
management. On the other hand, due to the
large amount of data expected to be stored
and managed in the manufacturing
environment, scale-out capabilities in big
data are needed. Finally, machine learning
modeling has increasingly become an
analytic capability mutually supplementing
the
traditional
first-principle-oriented
modeling. Introducing machine learning
capabilities
in
the
manufacturing
environment has become fruitful.
Analytical model frameworks offer a unified
execution framework that draws data from
the data framework below it, running
multiple analytic models as plug-ins
simultaneously and efficiently.
To complete closed feedback loops, insights
drawn from the data analytics are combined
with operational and business logics to
transform into actions. Often, there are
many applications involved in manufacturing
processes. To avoid building new chimney-
like closed applications, these applications
are run and managed in a unified application
development and operation (DevOps)
environment. Such an environment would
enhance the reliability of applications,
decrease the effort in application
development and reduce the complexity of
system operations and maintenance
management.
Built on such a broad set of technologies as
outlined above, an industrial internet
platform for the manufacturing environment
should seek to abstract a set of common
functions that are required and shared by
data-driven smart software applications and
offer them as horizontal platform services to
reduce
the
otherwise
repetitive
implementation of these functions in
conventional architectures. These key
common platform functions coincide with
core elements of the industrial internet,
namely data, analytical models and
applications (implementing business logics).
Furthermore, a Digital Twin framework
offers a unified, systematic approach to
represent, configure and manage the real-
world objects in the digital space. It also
provides a unified interface to the real-world
objects for application development, akin to
The data framework offers unified data
collecting,
processing
and
storing
capabilities to achieve full lifecycle
management of production data, avoiding
the data silos commonly found in existing
manufacturing environments.
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November 2019