The document presents a study on the application of artificial intelligence (AI) in the apparel industry, highlighting its potential in designing new garments, improving fit, and reducing environmental impacts. It discusses current uses of AI in fabric inspection, pattern making, and manufacturing efficiencies, with examples like Cognex's machine vision systems. The study concludes with the future implications of AI on sustainability and the textile sector, noting that commercial adoption is limited and primarily focused on larger manufacturers.

1. A Study on
Artificial Intelligence
in Apparel Industry:
Future of Technical
World
Paper Presentation done by
: Nandhinikutty

2. INTRODUCTI
ON
PRESENT
SCENARIO
FUTURE CONCLUSION
TABLE OF CONTENTS
01 03
02 04

3. INTRODUCTION
01

4. WHAT IS AI?
Artificial Intelligence (AI) is the
branch of computer sciences
that emphasizes the
development of intelligence
machines, thinking and working
like humans. For
example, speech recognition,
problem-solving, learning
and planning.
INTRODUCTION
Using a powerful algorithm that analyses past
designs and future trends, AI makes new apparel
designs complete with sewing patterns. Retailers
can choose to send AI-designed apparel straight to
manufacturing or incorporate this as an additional
step to automate the pattern making and fit
process.
Applications help both customers and
manufacturers sort out the fit situation, which
will make shoppers happier and reduce the
industry's environmental impact. Designers use
AI to create fabrics and garments; consulting firms

5. One example: Almossawi used AI to create a line of garments
based on the Japanese kimono. “I thought it would be cool to
look at designing different silhouettes with different kinds of
textures and details,” he said.
Eg: Designer Hussain Almossawi used AI to create a line of garments
AI HELPS WITH DESIGN
Almossawi taps AI for inspiration and idea
generation; it’s helped him generate more
ideas than he could without it. “As part of
every designer’s process, the early phase
involves a lot of explorations and ideation
sessions” ranging from bouncing around blue-
sky ideas to brainstorming with colleagues. AI,
he said, aids collaboration by expanding
person-to-person collaboration to human-to-
machine collaboration. “As crazy and
interesting as AI is, it’s probably just in its
infancy, and I can only see it getting better and
doing much more than outputting images,” he
said.

6. AI IN THE TEXTILE INDUSTRY
1. YARN MANUFACTURING
AI IN
THE
TEXTILE
INDUST
RY
2. FABRIC PATTERN INSPECTION
3. COLOR MANAGEMENT
4.FABRIC GRADING
5. AI IN PATTERN MAKING
6. SUPPLY CHAIN AND
MERCHANDISING

7. PRESENT
SCENARIO
02

8. Current Applications
Cognex – Fabric Pattern Inspection
Cognex Corp., founded in Boston in 1981
and with over a 1000 employees today is
an American manufacturer of machine
vision systems, software, and sensors.
The company offers its purportedly
machine vision-based Cognex ViDi
platform tailored for fabric pattern
recognition in the textiles industry.
Cognex claims that the Cognex ViDi platform
can automatically inspect aspects of
fabric patterns such as weaving, knitting,
braiding, finishing, and printing. The
company also suggests its platform
requires no development period for
integrating it into a manufacturing
system, and it can be trained using
Camera Based visual Fabr

9. Yarn Dye Plaid:-For this first woven fabric, we
provided our VIDI red tool with a representative set
of good samples for the system to learn by itself,
completely unsupervised, the weaving pattern, yarn
properties, colors and tolerable imperfections.
EXPERIMENT 2
Yarn Dye Stripes :-On this second set of
fabric, just as for the previous set, ViDi's red
tool learns, by itself, a model of the complex
knitting pattern from a collection of
randomly selected good samples.
CATEGORY 01
EXPERIMENT
1

10. Cognex ViDi distinguishes unacceptable defects on
seat belt and tire fabric while tolerating naturally
occurring variations.
EXPERIMENT 4 -
PRINTING
Cognex VIDI allows the inspection of printed
webs. It is able to identify problems in
printing quality such as misalignment of
different color channels (black outline versus
filling) while the motifs can be highly
complex.
CATEGORY 02
EXPERIMENT 3 -
WEAVING

11. Cognex ViDi can detect anomalies like soil or ink
spots on garments as well as defects in highly
critical stitching such as on airbags. It also excels at
verifying embossed characters on medical fabrics.
CATEGORY 03
EXPERIMENT 5 -
FINISHING

12. FUTURE
IN TEXTILE INDUSTRY
03

13. AI
IN
THE
NEAR
FUTUR
E
• In Mendelson’s view, some of the most
intriguing AI research and
experimentation that will have near-
future ramifications is happening in two
areas: “reinforcement” learning, which
deals in rewards and punishment rather
than labeled data; and generative
adversarial networks (GAN for short) that
allow computer algorithms to create
rather than merely assess by pitting two
nets against each other.
• The former is exemplified by the Go-
playing prowess of Google DeepMinds
Alpha Go Zero, the latter by original
image or audio generation that’s based
on learning about a certain subject like
celebrities or a particular type of music.
• On a far grander scale, AI is poised to
have a major effect on sustainability,

14. • There’s virtually no major industry modern AI — more
specifically, “narrow AI,” which performs objective
functions using data-trained models and often falls
into the categories of deep learning or machine
learning — hasn’t already affected. That’s especially
true in the past few years, as data collection and
analysis has ramped up considerably thanks to
robust IoT connectivity, the proliferation of connected
devices and ever-speedier computer processing.
• Some sectors are at the start of their AI journey,
others are veteran travelers. Both have a long way to
go. Regardless, the impact AI is having on our present
day lives is hard to ignore.
• With companies spending billions of dollars on AI
products and services annually, tech giants
like Google, Apple, Microsoft and Amazon spending
billions to create those products and services,
universities making AI a more prominent part of
their curricula, and the U.S. Department of Defense
upping its AI game, big things are bound to happen.
Some of those developments are well on their way to
being fully realized; some are merely theoretical and
Teslas Humanoid
Robot

15. CONCLUSION
04

16. In the last five years, academic research papers have been
published on using image-recognition technology in
the textile industry in a number of applications, such
as grading yarn appearance from the Textile
Department, Amirkabir University of Technology,
Iran or fabric-defect inspection using sensors. As
machine vision continues to make its way
into manufacturing and industrial applications, we can
expect to see more textile examination use cases in the
future.
Yet, commercial use of AI in pre-production textile
processing seems limited to only a few applications
today, particularly in identifying and grading textile
fibers and yarn. Fiber identification and grading in
terms of color, length, uniformity ratio, tenacity, etc.,
may see AI use cases develop in the years ahead.
We suspect that only larger and more tech-savvy textile
manufacturers are likely to adopt this technology in
the near-term, given the setup, integration, and the
potential need for data science talent that would be
required to successfully scale such an application

17. CREDITS: This presentation template was created
by Slidesgo, and includes icons by Flaticon, and
infographics & images by Freepik
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