At the end of this topic,
you will be able to explain
steel production processes and its steps,
implementing the digital twin in
the steel making production process,
describe the IIoT platform
and digital twin Framework based designs,
explain the real time monitoring
of our process control parameters.
In our previous discussion,
we explored how digital twin technology can
optimize production processes and quality management.
To further enhance our understanding,
let us now examine a case study of
the steel making process in a heavy industry.
Steel making is an important continuous process
in heavy industries, which is shown here.
After slabs blooms and
billets are made from molten steel,
they require forging and heat treatments.
The basic concepts and technologies of this scenario can
be applied to different steel making
or other similar operations.
Next, let us discuss the steps in steel making processes.
First, let's look at
the primary steel making production process.
The above figure provides information about
the various stages in
the primary production process of steel making,
which is called as upstream process.
The process of steel production is complex,
ongoing, and encompasses a wide range of steps.
Iron ore, coal, limestone,
and recycled steel are used to
produce both common and specialized steel.
Iron is produced in a coke oven,
blast furnace or a direct reduction furnace,
while steel is produced in
oxygen furnaces or an electric arc furnace.
Continuous casting is used to
produce slabs, blooms, and billets.
Next, let's take a look at the
secondary steel making production process.
In secondary manufacturing,
raw materials are transformed into pipes, sheets,
bars, rods,
and other structural shapes through
heating, forging, or pressing.
These shapes may undergo heat treatment,
surface coating, and finishing
before being shipped as finished products.
The figure shows each basic production process may have
multiple sub-processes supported by
production lines with complex machinery.
They also highlight the interaction
between upstream and downstream
parts of the supply chain.
The upstream processes have
an impact on the downstream ones.
The production of high end special steel requires
additional procedures and makes
quality monitoring and tracking very difficult.
Special steel is manufactured in small batches and is
used in equipment requiring high reliability and safety.
Hence, stricter quality standards are applied.
Traceability of product quality
throughout the production process is very essential.
We have a few examples here. Managing the process.
Speed of continuous casting and temperature of
molten steel are the detecting parameters,
inclusion that float on top of molten steel.
Visibility parameters, temperature is taken
into account during the rolling process
from beginning to the end.
Moving on to the implementation of
a digital twin in the steel making production process.
Controlling and monitoring industrial operations
is made possible with the help of
Internet of Things and
digital twin technologies
including programmable logical Controls,
TLCs, and supervisory control
and data acquisition, SCADA.
SCADA is a type of
monitoring software used for controlling
PLC hardware and recording data
even from remote locations.
The ISA-95 standard uses a layered model of
manufacturing technology and
a business process with four levels.
In recent decades, the steel industry has invested in
IT applications that align with the ISA-95 levels 0-4.
These systems manage various aspects of production,
such as production processes,
quality managed system, QMS,
control management system, production planning,
execution, MES, and equipment management system, EMS.
These investments have led to improved productivity,
quality, and cost efficiency in the steel industry.
Level 0, this defines the physical process.
Level 1 defines the sensing
and manipulating physical process.
Level 2 monitors and controls physical process.
Level 3 defines workflow tasks to produce end goods.
Level 4 defines business
related manufacturing management activities.
In the past few decades,
many heavy industries such as
the steel making industry
has been focusing on automation.
As a result, ISA 95 Levels 1 and
2 are primarily used for equipment automation.
Interconnected PLCs at ISA 95 Levels 1 and
2 make data collection very difficult.
PLCs connected to SCADA systems are typically separate.
Human machine interfaces are
used to view data and alarms.
SCADA data and alarms cannot
be easily connected with other systems.
Analyzing them takes more time
due to their technical content.
Automation and IT applications
are used to manage steel making operations.
Connectivity and integration issues
restrict further efficiency gains.
These hurdles are caused by
technical difficulties between production,
operation management application systems
and automation control systems.
The PLCs and SCADAs.
ID application and automation systems
encounter barriers such as
improper connections between PLCs
and SCADAs with application systems.
This hinders communication between many applications and
the production environment where
equipment is running and products are being processed.
Automation system data is incompletely collected and
used limiting production transparency
and data driven management.
The application systems such
as process, quality, equipment,
energy production planning and execution
impede manufacturing processes and
lines such as those
described in the steel making processes.
Let's discuss the other challenges on
implementing a digital twin
in a steel production process.
Industrial domain experts have
thorough knowledge of steel making
production processes and are capable of
transforming customer needs and
demands into business requirements.
Industrial software application analysts
translate business requirements
into software requirements and
assist in building software solutions.
Industrial data analytic engineers
with data modeling skills
can link data analytic requirements
into input data and modeling demands,
industrial control or automation engineers gather
project deployment structure and
equipment configuration information,
evaluate connection and data gathering needs,
and design IoT software solutions.
Furthermore, we will discuss
the steel production process using
an IoT platform and digital twin framework based design.
The displayed image is
the functional architecture of
UI Things Wise iDOS platform.
Online analytics depend on
collected data to improve online and batch quality.
Connectivity to equipment and
sensors to collect data through the Internet of Things.
The digital twin architecture has three layers,
a bottom layer, a middle layer, and a top layer.
Complete digital representation or
digital twins of the production environment,
including equipment twins,
product twins for tracking status and process data.
The dynamic relationship between
product introduction, PIPs and equipment.
The actual process data can be collected and
associated with the PIPs analytical capabilities
that can be embedded within the digital twins to analyze
data associations development ops setting in
which the applications that provide
the necessary business logic and
user interfaces may be built and operated.
First let's look at the bottom layer.
The Internet of Things allows for
the interconnection of devices,
the collection of information,
and the management of systems.
The following benefits are provided by IoT.
The connection of equipment,
collection of information, preparation of data,
input or output device information storage,
service for remotely managing gateways in IoT.
A digital twin framework is a set of tools used to
create a digital representation of a production setting,
store and manage information on production equipment,
and combine analytical models related to that machinery.
Designing a user interface for a digital twin,
data mapping for digital twins to the Internet of Things.
Analytical modeling using a Digital Twin,
open API for digital twins.
Moving on to the top layer,
a development and operations framework for creating,
deploying, and managing applications in
both codeless and code based graphic user interfaces.
A device for displaying data.
A streamlined method for creating
graphical user interface software development kit
that supports programming in multiple languages.
Microservice architecture.
The platform's IoT framework layer,
which includes IIoT application integration gateways,
enables equipment connectivity and data collection.
It also allows for integration with other systems,
such as visual configuration,
process specification management systems and
production management system without any coding required.
A digital twin layer is
built by digital twin designer application.
The digital twin layer sends the micro services to
the application framework layer
which enables the following business functions;
a real time monitoring of
quality and process quality, anomaly,
and a process exception handling,
lean quality management,
quality and process optimization analysis,
traceability of product quality.
All relevant data is fed into a digital twin platform.
Lean manufacturing and six Sigma concepts are recorded
for optimizing production processes
and quality management.
First, let's look at
the optimization of production processes.
Optimizing the production process with
the help of a digital twin is
nothing but using a process management tool
to collect, regulate,
and verify data obtained from
equipment and machinery, operations,
method, product and equipment specifications,
standards, production planning and quality design.
This figure shows
a special alloy making equipment and processes.
Here the billets and
other semi finished products are following
an optimized production path to complete
all other operations involved in steel making process.
The digital twin frame design
suggests the optimized production path by
considering all critical data and
constraints in each level of steel making process.
Multiple product manufacturing is frequent.
Dynamic production scheduling for
each product in production
maximizes the overall equipment effectiveness
and production capacity.
Each PiP can follow
a unique path in
the equipment matrix over its production lifespan.
Now we will be discussing about the quality management.
The key aspects of steel product quality are
structure performance, surface quality,
and geometric dimensions which depend
on material composition and processing methods.
To meet the needs of a client.
In quality of a product,
we should gather data on
quality attributes and process control design.
The recorded data from digital twin exhibits,
the real time measurements.
The real time measurement of a process and monitoring
the actual process are very useful
for quality measure and to satisfy the clients.
It is also useful for finding deviations.
Weak process control gives
bad impacts on quality of high-end special steel.
Now let's examine the real time monitoring
of a process control parameter,
which is obtained from a digital twin analysis.
This figure demonstrates the real time monitoring of
a process control parameters with statistics and alarms.
The main approaches of
lean management are eliminating waste,
standardizing processes and procedures,
implementing standard operations,
and continuous improvement.
By implementing a digital twin,
the following solutions are achieved in
manufacturing processes and quality management.
A standardized operation.
To standardize the workflow and procedures that deliver
quality products within
established limits and uncertainties,
we use software to maintain the standards.
Reduce waste, address quality issues
by reducing product faults and reworks.
Use data driven production control to maintain
process consistency and eliminate unnecessary variations.
Detect process failures and quality irregularities in
real time to prevent
downstream waste. Continuous improvement.
Regularly analyze quality performance using
standard statistical process
control methodologies based on
real time data so that
quality performance issues can
be identified and redeemed.
Conclusion of digital twin implementation.
The combination of IoT, digital twin,
and industrial analytics can
facilitate the digitalization of
industrial operations management including
manufacturing processes and product quality control.
Both equipment and product digital twins
are effective methods and
technologies for monitoring
product quality and
building quality traceability records.
Digital twins can reflect manufacturing value flows.
An IIoT platform with
a digital twin structure simplifies
the deployment of safe,
scalable, reliable industrial operation
and a quality management system.
By this, we have come to the end of this topic.
Let us meet in another interesting session. Thank you.