At the end of this topic,
you will be able to: Explain the digital twin,
benefits of a digital twin,
list the categories of the digital twin,
explain the advantages of having
a digital twin in manufacturing,
describe the digital twin in smart manufacturing,
list the procedure for implementing
a digital twin in any industry.
First, let's discuss the digital twin.
In 2002, Michael Grieves came up with the term
digital twin to describe
a new way to manage the life cycle of a product.
Because engineering, manufacturing, and
quality teams couldn't easily share processes and data,
the idea stumbled along for
many years before Industry 4.0.
Next, let us discuss the digital twin approach.
A digital twin is a virtual copy
of an object or a system that
stays up to date with real-time data
and help make decisions through simulation,
machine learning, and reasoning.
Now we will be learning
about the benefits of a digital twin.
Digital twins link production and performance,
providing valuable insights into manufacturing processes.
Digital twins help manufacturers to
accelerate digital transformation
and smart manufacturing.
Digital transformation efforts are becoming
more and more apparent as data integration,
artificial intelligence,
and the Internet of Things continue to improve.
Next, let us take a look at categories of digital twins.
Digital twins can be divided into
three categories: Product,
the creation of digital duplicates
for use in developing new products.
Production, manufacturing,
and production processes are
validated by using digital twins.
Performance, data collection analysis,
and decision-making using digital twins.
When all three types of
digital twins are combined and integrated,
it's called a digital thread.
Information gathering during the entire development and
manufacturing processes allows this
to be incorporated into other goods as well.
Now, let us take a look at
the digital twin in Smart manufacturing.
This figure here shows the details about
digital twin system of machine shop floors.
Here all process equipment workers
are all connected with main focused point,
that is, in time machine of cyber twin systems.
Next, let's discuss how manufacturers
use information or product data for digital twins.
Data or information obtained
from product performance distribution
and end-user experiences can
provide insights for digital twins.
So this leads to engineers and
designers to utilize this information,
to enhance product performance, process performance,
and quality, particularly in
terms of customization and user-friendliness.
Next, let's now learn about
digital twin in the manufacturing industry.
It examines the design implementation and
initial outcomes of an IoT platform embedded with
a digital twin framework for
the best manufacturing process control and
quality management based on a real-world use case.
Lean management and Six Sigma concepts are
based practices for developing a digital twin.
Manufacturing will adopt digital twins to
boost efficiency and lower maintenance expenses.
Integrating and analyzing data for
equipment and other production IT systems offers
real-time correlation and monitoring of
process design specifications and
actual process with quality data.
Now, we will discuss
the advantages of having a digital twin in manufacturing.
Digital twins also improve engineering,
product design efficiency,
strategy, and execution can produce remarkable results,
aviation is getting digital.
Digital twins help US Air Force
create a new aircraft prototype in one year.
Manufacturing digital twins
have saved millions elsewhere.
Digital twins reduce defective items
by analyzing various data sources,
minimizing delays, and enhancing production.
This concept goes beyond maintenance forecasting alone.
Next, let us discuss
the applications of digital twin for manufacturing.
First, product design.
During the design phase, digital twins can be
used as virtual prototypes that can change to
test different simulations or
designs before even spending money on a real prototype.
This saves time, money,
and because it can reduce on the number of times
the product needs to be changed
before it can be put into production.
Next, let's discuss production process optimization.
A digital twin of the production line can be constructed
using sensors to monitor and improve production targets.
By making adjustments to the digital twin,
one can better understand how to maximize output,
reduce variation, and get to the bottom of a problem.
Moving on to the quality management.
Keeping quality high and
reducing or eliminating rework requires
constant monitoring and acting on data from
sensors connected to the Internet
of Things throughout manufacturing.
The digital twin can simulate
the entire manufacturing procedure to
pinpoint problem areas and such as upgrades,
such as upgrading machinery or improving methods.
Next, let's discuss the supply chain management.
Companies in the logistics and
distribution sectors rely on
digital twins to monitor
performance measures like
the effectiveness of their packaging,
the effectiveness of their fleet,
and the efficiency of their delivery routes.
They shine when applied to
the problems of just-in-time and
just-in-sequence manufacturing and
the evaluation of distribution networks.
Moving on to the predictive maintenance.
Using a digital twin,
manufacturers can monitor the health of
particular pieces of machinery or processes.
They can see any discrepancies that can suggest a need
for maintenance or repair
before a major breakdown occurs.
They are used for adjusting load distributions,
tool calibrations, and cycle times.
Cross-discipline collaboration comes next.
With digital twins, operational
data is readily available,
facilitating cross-disciplinary cooperation,
enhanced communication, and quicker decision-making.
Data from engineering, manufacturing, sales,
and marketing may be shared,
allowing better collaboration and results.
Then, examine the customer service process.
A digital twin can provide data on
the performance distribution and
the end-user experience of a product.
Engineers and designers can utilize
this information to boost product satisfaction
through changes of features
like personalization and user-friendliness.
Moving on to the general procedure for
implementing digital twins in industries.
Integrating process design, quality,
specification equipments, real-time operational specs,
and quality measurement into
end-to-end closed-loop systems enables
online monitoring and analytics of
the production process and product quality traceability.
For integrating the above-said details
from various departments,
digital twins should be aligned
properly with industrial internet technology.
Online tracking of production processes and quality for
production equipment and products
is aligned with digital twins as well.
Create a digital lean capability
by fusing lean management and
Six Sigma principles and practices
with online data, and analytical tools.
During implementation of the digital twin
in the industry,
for optimizing the production process and quality,
the following challenges are
faced by machinists in various areas.
To collect the information from the below segments
in boosting the utilization of overall equipment,
in reducing scrap,
in collecting and processing the production data,
and utilizing data-driven decisions
to improve the production.
Moving on to how machinists approach
the challenges while implementing a digital twin concept.
Bring together and standardize
all sensor data and setpoint values,
calculating scrap and waste costs,
set up a digital copy of production space in an industry.
Let's look at the output observed by
the machinist in an industry to optimize
the production process and quality of a product
after implementation using a digital twin concept.
Output, reduced waste in scrap.
Waste is a major expense for industries
and even a small amount of waste can be very significant.
The digital twin concept helps
eliminate experimental waste and allows
for better resource utilization
ultimately reducing waste.
Quality control in real time.
Modifying a parameter in
real-world manufacturing can have
a significant impact on quality.
But manually setting up a quality control can take hours.
Digital twins provide real-time outcome modeling,
allowing for faster and more effective quality control.
Automatic settings for quality control.
Existing sensors and setpoint readings
can help the digital twin automate quality control.
Engineers can spend less time monitoring
production lines and more time
on high-value activities by
utilizing the proper technology to automatically alter
set points based on simulated
quality outcomes and real observations.
Total efficiency of the equipment or
overall equipment effectiveness, OEE.
Real-time and automated quality control provides
production equipment efficiency statistics and
relieves line employees and engineers of tedious jobs.
A newer, more reliable
machine can enhance production as well.
Complexity in production.
Manufacturing involves many complex variables.
Humans can't quickly process industrial data.
This data demands data-driven methods.
Production data improves production.
Now let's summarize what we have learned so far.
A digital twin is a virtual replica of an object or
a system that is constantly updated with real-time data.
Digital twins aid in the acceleration
of digital transformation and smart manufacturing.
The various applications of
digital twins in manufacturing,
such as product design, production process optimization,
quality management, supply chain management,
a predictive maintenance, cross-discipline collaboration,
and examination of customer service processes.
By this, we have come to the end of this topic.
Let us meet in another interesting session. Thank you.