Importance Of Machine Learning In The Textile Industry
Computers have been in use for textile manufacturing, but its advantages can be taken several steps further by integrating artificial intelligence into the system. While machine learning in the textile industry is still undergoing development, its earlier used cases are already showing significant signs on how they can drastically improve traditional garment manufacturing processes.
The use of AI is the most practical approach to future-proof your fashion business because other industries are also moving toward this direction. Here are some of the applications of artificial intelligence that show how promising the technology is once it reaches maturity:
1) Detecting defects
Fabric defects can reduce the value of the textile. Manually looking for flaws in the finished product is an impossible task because of the endless number of points that have to be observed and compared with the correct image.
Applying neural networks in machine learning in the textile industry help computers inspect fabrics and quickly identify defects. This works by having multiple base images of how a fabric should appear and compare it with the final product's extracted images.
2) Identifying patterns
Manually doing pattern inspection by human inspectors is an inefficient way to ensure product quality. Fatigue and subjective inspection of minor details can lead to errors that multiply in a large-scale production setting.
Installing a camera-based inspection system is one of the best applications of artificial intelligence in the textile industry. It can capture images of products in real-time and compare it with existing fabric pattern data.
Fabric patterns consist of several elements like weaves, prints, knits, and braids, and this challenges human controllers to identify and scrutinize each section of a finished product. On the other hand, feeding the AI-powered platform hundreds of samples enables it to learn more about weave patterns and yarn properties, making it easier to identify similar textile designs in the future.
But as an objective system, being too strict with pattern identification can lead to errors in recognizing which designs are similar and which aren’t. Like how humans inspect a fabric in detail, there are tolerable imperfections that are considered acceptable.
What makes machine learning in the textile industry flexible is how the platform can also be trained to accept defects within a certain margin of error. After feeding it with numerous good, bad, and acceptable samples, it will be able to differentiate completely wrong knit patterns from very slightly misaligned, but still acceptable designs.
A capable AI system can read through the complexity and variability of fabric patterns. It can make a systematic approach in segregating which finished products can be classified as rejects and which can be tagged as premium quality.
Frontier specializes in fabric pattern identification and uses it to search its massive database for the design you need. It can also search for similar patterns you may want to consider as alternatives in case the design you’re searching for is unavailable.
3) Matching colors
In the fashion industry, appearance is the foremost factor in attracting customers. It’s not the kind or the quality of the fabric used that immediately catches viewers' attention.
Color management is a vital aspect of the textile industry because it completes cloth designs. Even slight differences in color shades can become the reason for a batch pullout.
Fabric color is part of a fashion designer’s brand strategy, so making a mistake in color tinting can impact the reputation of the company.
For automated systems, the color comparison process is done by using numeric descriptions. This is also used to set the tolerable range for discrepancies in shades.
Back then, humans have to intervene and double-check the color matching results because machines had a lot of false positives in visual inspections. This causes redundancies in workload that lead to approval delays and higher overhead costs.
Improvements in machine learning in the textile industry have eliminated the issues of completely leaving color management solutions to automated platforms. AI-powered systems can now accurately match color descriptions, all while developing tolerances for infinitesimal discrepancies.
4) Fabric grading
Traditionally, fabric texture is graded by human vision and touch, which is prone to subjective testing. Advancements in machine learning in the textile industry have enabled a more objective fabric grading process that produces more consistent results.
The fineness, strength, and staple length of fabric fibers can be precisely measured using an artificial neural network. This can help you pick only the best stocks available without relying on the information relayed by your supplier.
Yarn prediction has also become more accurate by computing spins. The application of artificial intelligence has reduced error in predicting yarn grading by as much as 60%, resulting in better fabric grading. AI has made it easier to measure a textile’s physical properties and objectively classify fabric comfort.
The use of artificial intelligence in the textile industry is more likely to be seen in newer and more tech-savvy garment manufacturers because they understand the importance of data science.
Although the human element cannot be completely removed in the manufacturing, grading, and inspecting aspects of fabric processing, the integration of AI elements can exponentially improve efficiency, accuracy, and quality of the finished product.
Utilizing the Frontier AI
Fully utilizing machine learning in the textile industry can drastically reduce manufacturing costs and improve product quality. The technology is still in the infancy stage, but traditional garment manufacturers are seeing the clear advantages of using AI-powered systems. Soon, more companies will begin integrating the applications of artificial intelligence in their manufacturing processes.
Frontier is a cloud-based platform that uses an AI engine to accurately search our supplier database for the fabric pattern you need. The digital fabric swatches can also be used for 3D prototyping for a more convenient and efficient visualization process that can lead to faster product turnouts.
Both suppliers and fashion designers can benefit from the power of our AI-enhanced system to cut down logistics cost, expand the business network, access more extensive fabric design varieties, and churn out products quicker. By using digital swatches instead of physical samples, you can also contribute to sustainability efforts by reducing production wastes.
Simply register on our Frontier platform to experience how artificial intelligence in the apparel industry can benefit your business. If you still have questions regarding the system, contact us, and we can even schedule a demo for you.