Go to Admin » Appearance » Widgets » and move Gabfire Widget: Social into that MastheadOverlay zone
Garment and textile manufacturing have historically been labor-intensive industries, as seen in how many of world’s largest fashion, clothing and apparel brands seem to have a significant portion of their products manufactured in Asian countries such as China, India, Bangladesh, Vietnam, etc.
Much of the migration in textile manufacturing to the East happened over the past couple of decades when labor costs began rising in Asian countries (notably China). With increasing penetration of industrial automation in the industry, textile manufacturing businesses with access to historical and real-time operational data can leverage AI to improve efficiency and augment the capabilities of their human employees.
Readers should know that the adoption of AI applications in the textiles manufacturing industry is still very early, and although there are a few use cases (many of which we’ve explored in the sections below), there doesn’t appear to be widespread adoption of AI – even in developed countries.
Instead, we explore the possibility of applying artificial intelligence in the textiles industry today and what AI might be able to do for industry businesses two to five years in the future.
We’ve broken out the article below into current and future applications, examining companies and use cases individually to explore their business value
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 predefined images of what a good fabric sample looks like.
We could not find any list of successful use cases of Cognex’s ViDi technology at the time of writing. It is possible that the technology is in research and development or pilot phase, or that current customers have not given permission to be identified by name.
Based on the description provided by Cognex, the product seems to work as follows:
Below are a few snapshots from Cognex’s brochure illustrating its features, and what kinds of textile defects can potentially be detected by the machine vision system:
In the short 2-minute video below, a user demonstrates uploading “good” images of fabric samples in order to train the ViDi system to identify fabric errors:
From our preliminary research we found case-studies of Cognex vision systems being used in many industry sectors like automotive and pharmaceutical, yet there seems to be no such resource for the textiles industry.
According to Cognex, several challenges are inherent in inspecting fabric patterns, namely their complexity, variability and the sheer numbers of fabric types. Reto Wyss, Computer Science PhD and the CTO the Director of Software at Cognex was CTO at ViDi for 5 years before the first was acquired by Cognex.
Datacolor, founded in Lucerne, Switzerland in 1970 with over 380 employees offers color management instruments and software.
To ensure that the original design colors match the colors in a finished textile product businesses usually assign a “color tolerance” – a limit to how big the difference in color between a sample and the requirements of the customer can be, before the sample is considered acceptable. These tolerance values are generally agreed upon internally by the manufacturer or between supplier and customer to determine if the sample passes or fails inspection.
While traditional color tolerancing was done based on numeric descriptions of color through ”instrumental tolerancing systems”, that method generally had a lot of false positives compared with visual inspections, causing delays in the approval process because of the need for careful human intervention.
Datacolor claims it has developed an artificial intelligence Pass/Fail (P/F) feature to help improve the accuracy and efficiency of instrumental tolerance.
Datacolor suggests that its AI feature can take into account historical data of visual inspection results from human operators while creating the tolerances that in turn result in instrumental inspections matching more closely the samples of visual inspections.
Datacolor’s AI P/F procedure purportedly works as follows:
The snapshot below shows how textile manufacturers might use the platform to set tolerances for a number of manufactured batches for one customer. The green circle around the center of the graph represent the batches with “ideal” color values, thus passing the test and the yellow circle represent the acceptable tolerance limits:
In the real-world, this application might benefit both textile manufacturers and their customers to improve the speed and accuracy of the inspection processes for color matching.
For example:
At the time of this writing, Datacolor doesn’t seem to openly list it’s existing customers, and we were unable to find any case studies of their AI tool in use with any client company.
Datacolor’s software technology is currently led by VP R&D and Chief Technology Officer, Tae Park and Director of Research Michael Brill. Although we must add here that neither seem to have an explicit background in AI, though Michael Brill does hold a PhD in Physics from Syracuse University.
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 across a company.
The coming few years may possibly see the emergence of various vendors offering AI services for applications such as virtual modeling of yarn from fiber properties (Cornell), prediction of yarn tensile properties and yarn unevenness (Fraunhofer Institute).
A few examples of applications that businesses might see becoming commercialized in the future include:
Although a few AI vendors cater to the textile sector today, the number of use-cases and vendors is relatively low compared with other manufacturing sectors, and heavy industry broadly. Most AI applications in textiles today seem to involve the use of machine vision to replace or augment human examination of textile samples – usually in order to detect errors and anomalies.
It is promising to see several research-and-development activities being carried out at universities and other institutions – and we consider this to be a leading indicator in more potential industry use-cases in the years ahead.
It seems clear that real-world AI applications in the textile sector are still at a nascent stage, and it is possible that cutting-edge AI manufacturing applications are more likely to arrive in larger and more modern sectors – including electronics and automotive. One challenge might be the current lack of many system integrators and AI consultants specifically focused on the textile industry (again, possibly due to it’s relatively smaller size when compared to other global manufacturing sectors).
As we look ahead five years, AI might be capable of helping businesses in the textiles industry enhance quality, production, and lower costs. We expect that machine vision for textile inspection will be a low-hanging fruit use case and that a strong ROI from machine vision applications might encourage more enthusiasm and adoption for AI in general.
Businesses looking to leverage AI would do well to note that any such image processing application requires a large trove of existing data for the platform to learn from and that successful integration often involves a significant amount of time, costs and domain expertise from employees working alongside AI engineers – a dynamic that we’ve covered in depth in our AI enterprise adoption article.
Source: www.emerj.com