The Impact of Artificial Intelligence on the Textile Industry

This essay provides a comprehensive analysis of the application of Artificial Intelligence (AI) in the textile industry. It begins with an introduction highlighting the significant impact of AI on the global economy and its wide-ranging applications across various sectors. The essay then delves into the specific applications of AI in the textile industry, including automation, computer-controlled machinery, quality control, pattern inspection, and supply chain management. It discusses the use of AI in product costing, textile manufacturing, and just-in-time production. The essay also addresses the challenges faced in implementing AI, such as the need for significant investment, the importance of a collaborative mindset, and the ethical considerations related to data privacy and environmental sustainability. Overall, the essay emphasizes the transformative potential of AI in the textile industry, while also acknowledging the complexities and hurdles associated with its adoption.

Application of Artificial intelligence in textile industry
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Introduction
Artificial intelligence has had a huge impact on people's lives as well as the economy.
Artificial intelligence has the potential to add $15.7 trillion to the global economy by 2030.
When a company think about it, it's nearly the same as China and India's current economic
output. From tracking asteroids and other cosmic bodies in space to predicting diseases on
Earth, developing new and novel ways to combat terrorism, and making industrial designs,
AI has a wide range of applications. Like many other industries, the fashion business has
benefited from the Fourth Industrial Revolution, which has enabled it to gather and use data
that was previously technically or economically unfeasible. Since the birth of human history,
technology has always been a part of tailoring and has affected all human activities. Making
clothing, like other crafts, demands not just specialised tools but also specialised knowledge
of how to use them properly. This essay focuses on evaluating the application of Artificial
intelligence in textile industry. It also includes the challenges faced by the companies in the
application of Artificial intelligence in textile industry.
Application of Artificial intelligence in textile industry
According to a report on M Shahbandeh's Statista, global apparel and footwear demand is
anticipated to expand from US $ 1.5 trillion in 2020 to over US $ 2.25 trillion in 2025. It is
well acknowledged that the situation is deteriorating. The textile industry is turning to
automation and artificial intelligence (AI) to meet client needs while saving labour and
manufacturing costs as the demand for high-quality products grows. The conventional labor-
intensive textile business has been transformed by the rise of new technologies such as
artificial intelligence (AI) and the Internet of Things (IoT). The majority of textile industries
now use computer-controlled machinery, and large-scale production of specific designs is far
more efficient than human labour (Wang et al., 2020).
Textile mills are rethinking their entire AI manufacturing process as well as corporate
management. AI can access and collect historical and operational data in real time to deliver
insights and boost operational efficiency. Companies can adjust processes and develop
human talents more easily when they have a thorough grasp of the business. AI has an impact
on product costs, textile manufacturing, quality control, just-in-time production, data
collecting, computer integrated manufacturing, and so on (Shi et al., 2020). Embedded AI

applications include defect identification, pattern inspection, and textile colour matching, to
name a few.
Artificial neural networks (ANN) make detecting flaws in models like weaving and
organising more easier. Inspections provided can help to eliminate human errors and hence
increase efficiency (Giri et al., 2010). Cognex VIDI is an example of AI technology that was
mostly invented by COGNEX Corp. It infects the organization-faced tissue pattern and
eliminates production by lowering the lowest workload and highest accuracy pattern faults.
By working with the quality of each material quality, AI may also be utilised to detect tissue
faults in the past. Manual detection takes time, and it's tough to speed up low-intrusion
operations. Datacolor recommends that AI capabilities be used to retrieve historical data from
human operators' visual evaluation results and produce margins of error. This makes it
possible to conduct inspections that nearly resemble the visual inspection pattern. Color dyes,
liquid pigments, pastes, and media are all available through Data Color System Dispensers. In
a short period of time, get the most correct solutions in a number of fields.
Machine learning has enabled textile manufacturers to evaluate materials more objectively
and deliver more consistent outcomes. Artificial neural networks are used to reliably measure
the lengths of thin, solid staple fibres. High-performance LED light bars are made on
machines with powerful coupled high-resolution cameras and electric motors in 90% of the
cases (Riahi et al., 2021). It's used to keep track of chemicals. Pattern cutting and design
creating is a crucial procedure in the textile industry, in which the fabric is cut into a certain
design and numerous patterns are made on it. CAD is a subset of AI that allows designers to
generate digital patterns that can create and digitise a pattern's basic structure. AI can be used
to automate shipping and packing in the textile sector. The seamless exchange of materials
between distributors and producers is dependent on supply chain management. Large storage
areas, greater warehouse management, product separation, and better communication are all
required for good supply chain management. Robots, RPA, machine learning, IoT, and other
AI-based technologies can deliver all of these advantages.
Challenges in the Application of Artificial intelligence in textile industry
Business is booming with 3D technology, which has the potential to change the fashion
industry. Fast fashion trends have already altered the industry's seasonality, and 3D printing
has the potential to speed up manufacturing and shorten time to market. Customers may also

be able to assist in the sizing and design of garments. In some circumstances, 3D can also aid
with efficiency and cost savings. Consider the expenditures involved when the design team
prepares a sample or when the sample is manufactured in China and shipped back to the
United States (Karam, Zaher and Mahmoud, 2020). Then take a look at how many
procedures still rely on the old Excel spreadsheet. There has to be a better way to go about
things. Artificial intelligence will play a larger role in the coming years as the need for
efficiency and competition in various industries grows. Brands can improve their company
performance by experimenting with and researching technology, increasing the user
experience and, as a result, offering value to their consumers more effectively.
Incorporating virtual technology into everyday routines may appear to be a simple mission to
gain a competitive advantage, but it typically necessitates the cooperation of many people
across several departments and legacy systems. When it comes to innovation initiatives,
mindset is the most critical factor that can make or break a company (Huo, Tang and Kim,
2019). The term "innovation" refers to experimenting with new ideas, and not all assessments
will be successful on the first try. Professionals are often hesitant to try out new and
innovative ideas because they may have an impact on their return on investment. A failure
could result in a lower ROI and, as a result, lower profitability. However, it's far more
difficult to associate the ROI with emerging technologies, and a virtual transformation
journey necessitates a unique set of indicators, such as engagement or productivity dynamics.
Every company transformation must be done on multiple levels. Everything begins with a
height decision and a budget plan, and hundreds of staff adjust to new ways of working and
operating to accommodate the transition from the previous approach. Because some of the
new development methods require the creation of room for new ones, handling new systems
is costly. Z generation desired new technical experiences, but since the epidemic began,
consumers have been thinking about the environment as well. Some garment firms, for
example, have employed traceability to give block chain technology so that customers can
track the life of their clothes and what happened when they returned it. Furthermore, the
growth of new social media platforms such as TikTok will provide businesses with more
opportunities to communicate with their customers. Gen Z accounts for 60% of TikTok users.
Many members of this generation are concerned about the environment and support brands
that encourage ecologically friendly production processes. Brands must employ these social
media strategies to make behind-the-scenes movies of the sustainable measures merchants
use in their operations to meet consumer demand and enhance brand loyalty. Yes, there is.

For most developers, the amount of power required by these resource-intensive algorithms is
a deterrent. Machine learning and deep learning are the foundations of artificial intelligence,
and their efficient operation necessitates an increasing number of cores and GPUs
(Yuldoshev, Tursunov and Qozoqov, 2018). There are several fields with concepts and
information for implementing deep learning frameworks, including: B. Tracking asteroids,
deploying health care, tracking the universe's body, and so on. The unclear method deep
learning models predict output is one of the primary worries concerning AI. A layperson may
find it challenging to comprehend how a particular set of inputs might result in solutions to
various types of issues.
Conclusion
The rise of new technologies such as artificial intelligence (AI) and the Internet of Things has
changed the textile industry, which was formerly labor-intensive (IoT). Computer-controlled
technology is currently used by the majority of textile industries, and large-scale production
of a design is far more efficient than human labour. It's easier to change processes and
develop people's skills when a corporation has a thorough understanding of the business.
Artificial intelligence affects product cost, textile manufacturing, quality control, just-in-time
production, data collection, computer integrated manufacturing, and other areas. Machine
learning allows textile manufacturers to evaluate materials more objectively and offer more
consistent results.

References
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