How AI Can Identify Defects in Fabric
With the increasing prevalence of technology in manufacturing, the detection of defects in fabric can improve cost and time efficiency. Factoring in extensive labor costs of past detection methods and potential human error, the use of automation technology and AI represent a noticeable step up.
This article will provide insight into how AI can optimize the detection of defects in fabric and optimize production for the textile industry.
How Artificial Intelligence Improves Defect Detection
Aside from helping with labor costs and improving efficiency in results, AI boasts a consistent accuracy in various defect detection methods that outperform past manual and labor-intensive processes.
Furthermore, AI has learning capabilities that allow its performance to improve detection results. As a result, the use cases of this technology are practically endless. Not only can it adopt many methods used in the past, but it can also develop methods of detection not possible through manual labor.
From another point of view, using automated technology to detect defects means more consistent results as it can perform its duties twenty-four hours per day. The level of adaptability that AI technology is capable of will outperform any hands-on approach.
Technology like this is on its way to making error rates and labor costs outdated concepts. Aside from the accuracy in defect detection, automated technology and AI can provide more results in a fraction of the time compared to manual labor.
Optimizing Defect Detection Methods
AI can incorporate present defect detection methods while picking out specific defects layer by layer with speed. Many automated methods include image analysis that surpasses the human eye’s capability.
Moreover, as mentioned earlier, manual defect detection methods include human error. Whether large or small, it’s an issue that can’t be entirely avoided, especially considering the demanding work ethic of the textile industry.
Of course, in many cases, to minimize human error, you have to reduce production speed. Additionally, AI can learn from its mistakes without repeating the same errors, further optimizing the textile industry’s output.
With the use of AI, you can also implement many different detection methods, allowing you to cover the many other fabric defects known to the industry. Even when implementing multiple AI detection methods, it won’t come anywhere close to manual labor costs.
The Evolution Rate of Automated Technology
Until recently, manual detection methods evolved from expanding knowledge with hands-on experience. Even with the utmost human capability, this simply takes time. However, AI is improving at an extremely rapid rate that far surpasses what manual detection methods are capable of.
From a business perspective, this level of accuracy in results and efficiency in performance is a substantial competitive advantage.
Considering AI’s increasing popularity worldwide, it’s vital to consider its advantages for fabric defect detection. More automated methods for defect detection are being created as time moves on, and its adoption rate is increasing as well.
In addition, automated technology can break down fabric layer by layer regardless of thickness or contrast, which leads to more detailed results that manual inspections may miss.
Deep learning technology in AI can extract much more information than what can be found efficiently through manual detection methods. Although some approaches are still being researched and need some work, AI’s development in deep learning with image analysis of fabric defects is a far superior approach.
Furthermore, inevitable human error tends to incorporate small-scale defects that are hard to catch in addition to the speed of production. Automation technology can extract multi-scale features in the fabric and then learn from repeated or similar patterns found in collected data and imaging to improve future results.
Types of Fabric Defects
Another issue with manual detection methods is that it’s difficult to inspect every type of defect category, as the list is quite extensive. Moreover, through manual techniques, some defects need more time to be inspected than others and can lead to more human error and delays in production.
Over the years, many fabric defects have gone unnoticed due to production requirements and the capabilities of manual labor.
AI has the capability to detect every type of fabric defect with efficiency and accuracy while providing more detailed data on the defect. This information will lead to better quality control and overall cost. Below is a list of common fabric defects that automated technology can detect:
● Shade variation
● Drop stitching
● Crease Marks
● Needle lines
● And many more
Although a professional may be more than equipped to detect these fabric defects, these methods simply can’t keep up with current production demands. Considering the many different types of defects, this also requires detection methods that can adapt to many different contexts.
Not all fabric anomalies are the same, and implementing multiple detection methods is often necessary for the most optimal outcome. The world of technology and automation is moving faster than ever, and AI is creeping its way into many different industries, especially manufacturing and production.
Issues such as labor costs, production efficiency, and detection errors can end up building a snowball effect of problems, only causing more delays in production down the road.
An Unavoidable Future with Artificial Intelligence
Longevity is important to any business, and whether the textile industry embraces it or not, many competing companies and industries will be using these technologies as soon as possible.
Technology will continue to find its way into every part of life, and automated technology has a fantastic ability to optimize production, cut costs, and improve quality control. It seems like a wise approach from these factors alone, but that isn’t to say that AI can’t be improved.
Even with what’s currently available, plenty of research is being done on fabric defect detection methods using deep learning technology. However, aside from the many benefits that technology provides, this doesn’t mean manual detection methods will become entirely obsolete.
Instead, these same professionals may need to provide input into the development of AI and monitor their results for accuracy to ensure the AI detection works.
It’s not that manual detection methods don’t have validity; it’s simply due to the nature of our current world economy and the demand in manufacturing and production. This is what leads to more comprehensive quality control, and unfortunately, manual detection takes time which is a commodity in itself in the textile industry.
Further Improving Fabric Defect Detection
It’s clear that automated defect detection has plenty of upsides, but this will still require manual detection to some degree for the best results. Businesses will still need trained professionals to operate and check the results of automated defect detection.
Even if AI can provide over 96% accuracy through image analysis, there’s still 4% that goes unaccounted for at the present moment. Furthermore, relying solely on manual defect detection is an outdated concept that won’t keep up with future production and quality control requirements.
In addition, new developments towards automated fabric defect detection are being created at a rapid rate, and it won’t be challenging to adopt new technologies to current production and defect detection practices.
Efficiency and quality control are critical in a business such as the textile industry, and AI will give a company the ability to streamline production as a whole.
Furthermore, AI can detect defects in multiple fabrics simultaneously while singling out each defect, even if they’re all different categories. Of course, with technology like image analysis, other factors could affect performance, such as lighting, specific fabric colors, or particular contrasts in the images.
These particular factors are being improved on a consistent basis, and AI technology is being developed at a rapid rate to solve those problems.
How Simerse Is The Best Option for AI Implementation
Artificial intelligence is developing rapidly in many ways, and it can be difficult for any individual or business to keep up with what’s currently possible with this technology.
Implementing AI technology into your defect detection methods will come with a substantial learning curve, and you must have professionals with the proper experience to guide you through this process.
With years of experience in developing and implementing defect detection technologies, Simerse has a long track record of providing automated solutions to optimize business operations.
As mentioned previously in this article, many automated detection methods are still being developed to improve quality and performance, but that doesn’t mean you should wait to get started.
You can expect a plethora of knowledge and expertise from Simerse in all things related to automated technologies and artificial intelligence. With an endless list of use cases, AI can mold to your business’s needs and adapt to any changes in operation with minimal effort.
Although each company in the textile industry may have different requirements, Simerse can provide custom AI-focused solutions that’ll improve any fabric defect detection method currently in use.