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Many industries have now taken advantage of AI (Artificial Intelligence) or artificial intelligence technology to support business growth. One of them is the textile industry, where the industry is now slowly adopting AI and automation technologies to transform production, manufacturing processes, customer relationships, and more.

Many industries have now taken advantage of AI (Artificial Intelligence) or artificial intelligence technology to support business growth.

One of them is the textile industry, where the industry is now slowly adopting AI and automation technologies to transform production, manufacturing processes, customer relationships, and more.

According to a study from Statista, the global textile market is predicted to grow in value from US$1.5 trillion in 2020 to around US$2.25 trillion in 2025. This indicates that the demand for products from the textile industry will continue to increase worldwide.

The increasing demand for quality products has driven the textile industry to use AI and automation technologies to minimize labor and production costs, as well as to deliver products according to customer preferences.

Textile itself is a labor-intensive industry, and technologies such as AI and IoT (Internet of Things) have now helped the industry a lot such as in terms of easier data processing, predictive analytics, developing smart clothes, and efficiently performing work in the production process without any hassle. human intervention.

Furthermore, the following are some examples of the application of AI technology that has been carried out in the textile industry:

1. Identifying Defects in Fabric

Defects in the fabric can reduce the value of textile products. Any defects in the fabric that are passed on to final production, may result in customer rejection. That is why it is very important to check the quality of the fabric before the production process.

Generally, fabric inspection is carried out manually by specialized workers in the factory using lighted tables equipped with special equipment. This process is relatively slow and there is still the potential for fabric defects to be missed during inspection.

In this case, the use of AI can perform these tasks faster, with much higher accuracy, and without fatigue than using human power.

AI can also be used to predict fabric properties before the production process with the help of systems such as neuro-fuzzy or others using fabric construction data.

2. Fabric Pattern Inspection

AI technologies such as Artificial Neural Network (ANN) assist in the detection of defects in patterns in fabric production processes such as weaving and knitting.

AI-enabled vision-based inspection can reduce human error and increase efficiency.

For example, the Cognex ViDi solution developed by Cognex Corp. can automatically check the fabric pattern automatically.

The Cognex ViDi solution leverages machine vision based technology and does not require a specific development strategy to implement it into manufacturing.

In this way, AI-enabled fabric pattern inspection will accelerate manufacturing in reducing pattern defects with minimum labor, as well as maximum precision and accuracy.

3. Color Match

Color is an important aspect of textile products. The appearance of a textile product is perceived to be related to its quality.

The color of a product is rated as acceptable/unsatisfactory, or can be rated in more detail as: 'too light' or, 'too dark', 'too red' or, 'too green'.

To overcome this problem, an AI can be developed that has a 'Pass/Fail' feature to help improve accuracy and efficiency.

4. Monitoring Stitches

In the production process in the textile industry, stitching is done to unite two or more pieces of fabric.

Ease of stitching and stitch performance are important parameters known as “sewing capability.”

The low-tensile mechanical properties of the fabric such as tensile, shearing, bending, etc. can affect the sewing ability.

An AI-based system can be used to discover the sewing capabilities of different fabrics during production. Of course, ensuring the production process runs smoothly.

5. CAD system

One of the important stages in textile production is cutting and pattern making wherein the fabric is cut according to the design and makes different patterns on the fabric.

Computer-Aided Design (CAD) systems are a subset of AI that enables computerized pattern creation.

With this system, designers can create the basic structure of a pattern and digitize it. CAD is used in cutting patterns where it provides a 3D drawing of the fabric and design which makes visualization easier.

6. Production Planning and Control

Production Planning and Control (PPC) is an important matter where in this case there is coordination between various production departments so that delivery dates can be met and customer orders can be delivered on time.

AI technology can be used to complete machine layout, operation assignment, sewing process balancing, and more. That way, AI can help in achieving the main goals of PPC.

7. Final inspection

Inspection of finished and semi-finished textile products during production is very important to get fewer failed products.

The final quality check of the finished garment is usually carried out by an experienced person, which is very time consuming and may be affected by the physical and mental condition of the examiner.

To overcome this, the use of AI can be applied to achieve efficiency and accurate results. Automated inspections can be carried out using AI with image processing to check the quality of each product in detail.

8. Supply Chain Management

Supply Chain Management (SCM) integrates various business processes, activities, information, and resources to create value for customers. Standard-compliant SCM can help manage costs and improve business competitiveness.

In this case, for example AI technology can be used to automate transportation and packaging in the textile industry.

SCM is important for managing the smooth flow of materials between retailers and manufacturers. Good SCM demands large storage space, better warehouse management, product separation, and better communication.

AI technology can deliver all of these benefits through robotics, RPA, machine learning, IoT, and other cutting-edge technologies.