Smart manufacturing

BIRLA INSTITUTE OF TECHNOLOGY
PRESENTATION OF
SMART MANUFACTURING
ON
SMART MANUFACTURING
BY,
NAWAL SINHA
MT/EE/10024/19

CONTENTS
 INTRODUCTION
 WHAT ARE ENABLERS ?
 BIG DATA ANALYSIS IN SM
 INDUSTRIAL INTERNET OFTHINGS
 BLOCK CHAIN MANUFACTURING
 ADANCED ROBOTICS
 DIGITALTWIN
 SMART FACTORY
 ADVANTAGES AND DISADVANTAGES
 CONCLUSION
 REFERENCES

INTRODUCTION
 Smart manufacturing is a broad category of manufacturing that
employs computer integrated manufacturing, high levels of
adaptability and rapid design changes, digital information
technology, and more flexible technical workforce training.
 Other goals sometimes include fast changes in production
levels based on demand, optimization of the supply chain,
efficient production and recyclability
 Smart manufacturing (SM) is a technology-driven approach
that utilizes Internet-connected machinery to monitor the
production process. The goal of SM is to identify opportunities
for automating operations and use data to improve
manufacturing performance.

 SM is a specific application of the Industrial Internet of
Things (IIoT). Deployments involve embedding sensors in
manufacturing machines to collect data on their operational
status and performance. In the past, that information typically
was kept in local databases on individual devices.
 Now, by analyzing the data manufacturing engineers and data
analysts can look for signs that particular parts may fail,
enabling preventive maintenance to avoid unplanned
downtime on devices.
 For example, SM systems might be able to automatically
order more raw materials as the supplies, allocate other
equipment to production jobs as needed to complete orders
and prepare distribution networks once orders are completed.

WHAT ARE ENABLERS ?
 Smart manufacturing is a combination of various technologies and
solutions which collectively, if implemented in a manufacturing
ecosystem, then termed as smart manufacturing.
 We call these technologies and solutions "enablers," which help in
optimizing the entire manufacturing process and thus increase overall
profits.
 Some of the prominent enablers in the current market scenario include:
 Artificial intelligence
 Block chain in manufacturing
 Industrial internet of things
 Robotics
 Condition monitoring
 Cyber security

Big Data Analysis in SM
 Smart manufacturing utilizes big data, to refine complicated
processes and manage supply chain .
 Big data analytics refers to a method for gathering and
understanding large data sets in terms of what are known as
the three V's, velocity, variety and volume.
 Velocity informs the frequency of data acquisition, which can
be concurrent with the application of previous data.
 Variety describes the different types of data that may be
handled. Volume represents the amount of data.
 Big data analytics allows an enterprise to use smart
manufacturing to predict demand and the need for design
changes rather than reacting to orders placed.

 Artificial intelligence/machine learning – The data generated from
big data processing enables automatic decision-making based on the
reams of data that manufacturing companies collect. AI/ML can
analyze all this data and make intelligent decisions based on the
inputted information.
Fig. 1:Real time monitoring using data analytics
 From the above fig. we can say analyze how data analytics is being
used for the inspection of product quality. If there is any problem
with the quality then the signals are send to identify problem and
then again rework is done.

INDUSTRIAL IoT (IIoT)
 IIoT is nothing but an ecosystem where every device, machine
and/or process is connected through data communication
systems.
 Each machine and piece of industrial equipment is embedded
or connected with sensors which typically generate the
relevant data.
 This is further transferred to the cloud/software systems.
 This huge amount of data has lots of insight which if analyzed
may help in identifying certain dark areas within the
production process.
 After the analysis of the data, it is sent as feedback to the
production systems for any corrective action.
 Major forces driving IoT in market are the growing need for
centralized monitoring and predictive maintenance of
manufacturing infrastructure.

 The increasing need for agile production, operational efficiency, and
control, and demand-driven supply chain and connected logistics are
also expected to drive the market.
Fig 2: IIoT in factories
 From the above figure we can see that how the majority of work in a
factory is being done by the cobots and all the data is there in predictive
analysis. There are IoT sensors that are used to detect whether the
equipment is correct or not and then packaging is done by cobots.

Block Chain Manufacturing
 Block chain can act as a “single source of truth” for all the entities
(subsidiaries, partners, etc.) doing purchases on your behalf and
negotiating different terms with suppliers.
 A block chain-based database can store relevant data from all the
partners, giving the company a 360-view of the total volume of
purchases, regardless of who managed the purchase activity.
 There will be no need for individual users to constantly share
operational data and someone else to crosscheck it — the audits will
be conducted automatically, eliminating the resource-heavy
processes such as extra price verification.
 Some of the industries that are actively developing block chain
include apparel, solar energy, mining, fishing, food & beverage,
shipping (cargo transportation), fertilizer, healthcare and aviation.

Advanced Robotics
 Advanced industrial robots, also known as smart machines operate
autonomously and can communicate directly with manufacturing
systems. In some advanced manufacturing contexts, they can work
with humans for co-assembly tasks.
 By evaluating sensory input and distinguishing between different
product configurations, these machines are able to solve problems
and make decisions independent of people.
 These robots are able to complete work beyond what they were
initially programmed to do and have artificial intelligence that
allows them to learn from experience.
 These machines have the flexibility to be reconfigured and re-
purposed.

 This gives them the ability to respond rapidly to design changes and
innovation, which is a competitive advantage over more traditional
manufacturing processes
 There are Cobots ,the robots that can work collaboratively with the
people. So , the robots can work with the machines directly or with
the people.
Fig 3: Robots working with the machines

Digital Twin
 Digital twin is another concept in the ecosystems of smart
manufacturing.
 It creates the virtual model of an asset, process, or system
by using the data obtained from sensors in the systems or
asset and algorithms for making reasonable projections
about the process.
 Predictive maintenance is one of the important systems
which will use digital twins.
 The benefits of digital twins include potential reduction in
time and cost of product development and elimination of
unplanned downtime.
 The rising adoption of IoT and cloud platforms, and 3D
printing and 3D simulation software are boosting the
adoption of digital twin.

 Aerospace & defense, automotive & transportation, electronics &
electrical/machine manufacturing, and energy & utility are the major
adopter of digital twins.
 Once the concept of digital twins develops and matures, then we
may see its increasing application in non-manufacturing sectors such
as retail & the consumer goods market.
Fig.4: Digital twin of human hand
 From the figure we can see that the virtual hand is touching human
hand. The projections of human hand is there virtually that is known
as digital twin of human hand.

Smart Factory
 Smart Factory is the vision of a production environment in
which production facilities and logistics systems are organized
without human intervention
 The machinery and equipment are able to improve processes
through automation and self-optimization.
 The benefits also extend beyond just the physical production
of goods and into functions like planning, supply chain
logistics, and even product development.
 The technical foundations on which the Smart Factory - is
based on are :
 cyber-physical systems that communicate with each other
using the Internet of Things and Services.

 An important part of this process is the exchange of data
between the product and the production line.
 This enables a much more efficient connection of the Supply
Chain and better organization within any production
environment.
 The manufacturing information provided by the product on
an RDIF chip in machine-readable form, for example, can be
used to control the product's path through the individual
manufacturing steps.
 Other transmission technologies, such as WLAN or QR codes,
are also possible.
 The smart factory can reduce costs and material waste, and
requires fewer people with smaller operating costs.
 It’s more agile, delivering high quality and fast production that
is responsive to customer needs, providing the business with
more granular and up-to-date information.

Fig:5 Cobots working in manufacturing industries
 In the above figure we can see that there are only cobots that are
working in the manufacturing industry. All the process like
packaging, inspection, manufacturing is done by the cobots .
 The results are automatically generated and is being stored in the
cloud. From where it is being used is ordering more materials, or
checking the faulty lines of manufacturing.

Advantages of smart manufacturing
 Increased productivity
 These extremely adaptable systems enable greater flexibility.
 In terms of efficiency, one of the main savings comes from the
reduction in production downtime, hence efficiency is improved.
 Predictive AI technology can highlight problems before they
occur and take steps to mitigate the financial costs. long-term
cost savings.
Disadvantages of smart manufacturing
 Upfront cost of implementation. As many small to midsize
companies won't be able to afford the considerable expense of
the technology.
 The technology is very complex, which means that systems that
are poorly designed or not adequate for a particular operation
could not make profits.

Conclusion
 We have seen that how smart manufacturing is changing the world.
In next few years a lot of human jobs will be in danger as all the
works will be done in an automated matter by the robots.
 As already robots have been developed to collaborate with the
humans or to collaborate directly with the machines using artificial
intelligence.
 The digital twins has also changes the face of the IT world by
creating a virtual word and imitating things.
 The applications of smart manufacturing are very large for eg. In
agriculture also agricultural robots can do work, there are smart
factories that are designed that includes smart buildings, smart
offices, smart industries, smart shops etc.
 Smart manufacturing is also widely used in tracking system.

References
 Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent
Manufacturing in the Context of Industry 4.0: A Review. Frontiers of
Mechanical Engineering. In press.
 Kang HS, Lee JY, Choi SS, Kim H, Park JH, Son JY, Kim BH, Noh SD
(2016) Smart manufacturing: past research, present findings, and future
directions. Int J Precis Eng Manuf-Green Technol
 Kusiak A (2018) Smart manufacturing. Int J Prod Res 56(1-2): 508–517.
https://doi.org/10.1080/00207543.2017.1351644 3. Li B-h et al (2017)
Applications of artificial intelligence in intelligent manufacturing: a
review. Front Inf Technol Elect
 https://internetofthingsagenda.techtarget.com/definition/smart-
manufacturing-SM
 https://blog.marketresearch.com/the-top-7-things-to-know-about-smart-
manufacturing
 https://en.wikipedia.org/wiki/Smart_manufacturing
 https://www.networkworld.com/article/3280225/what-is-digital-twin-
technology-and-why-it-matters.html

Smart manufacturing

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