News & Insights | Textile Technology

Eurecat and Canmartex set up spin-off Aracne specialising in predictive quality control systems for knitwear textile manufacturing

Published: October 31, 2023
  • The Eurecat and Canmartex spin-off markets an innovative patented system which predicts potential manufacturing faults before the fabric is made.
  • The predictive system harnesses technologies including photonics, the Internet of Things and artificial intelligence and makes it possible to cut production defects by more than 50%, thus fostering the circular economy.
  • Aracne has a business plan which envisages the market release of its first product in the second half of 2023 with a sales forecast of €6 million by 2025.

Barcelona, 31 October 2023.- The Eurecat technology centre and textile business Canmartex have set up the spin-off Aracne, which is to deliver predictive quality control systems for the knitwear manufacturing process. Its business plan envisages the market release of its first product in the second half of 2023 with a sales forecast of €6 million by 2025.

The Aracne system predicts knitting faults in large-diameter circular knitting machines caused by broken or worn machine parts such as needles and platens, thereby reducing production defects by more than 50% and fostering the circular economy.

It also monitors the drop of fabric when it is being manufactured to spot defects appearing at that time due to other reasons, such as yarn or mechanical problems or holes in the fabric.

“The key difference with respect to other technologies already on the market is that Aracne’s technology is a predictive system which anticipates defects” by harnessing “photonic and Internet of Things systems coupled with artificial intelligence algorithms which predict potential manufacturing faults before the fabric is made,” says Xavier Plantà, director of Eurecat’s Industrial Area and the spin-off’s CTO.

“The ability to predict defects makes it possible to warn the production manager, who can then assess them and decide what to do,” notes Enric Martí, the spin-off’s CEO. This means “we can anticipate faults before they happen and most importantly take action and remedy the causes which lead to them.”

The innovation, which has been developed specifically for large-diameter circular knitting machines in partnership with textile machine manufacturer Canmartex, “helps drive the industry’s sustainability, boost production and cut costs while enhancing the innovative positioning of businesses,” adds Josep Maria Serres, a researcher in Eurecat’s Advanced Manufacturing Systems Unit.

Real-time analysis of the most critical components

An estimated 92 million tons of waste is generated annually by the textile industry, 25 percent of which is produced during fabric manufacture. Here, Aracne’s technological solution enables real-time analysis of the degradation of the most critical components in the manufacturing process.

The idea is to carry out predictive control using digital technologies to prevent typical quality problems or defects such as holes, streaks and spots which are generally only identified after the manufacturing process by experts or automated computer vision-based systems once they have already occurred.

The innovation Aracne brings to the market as a result of Eurecat’s technology transfer and valorisation has a direct impact on knitwear weavers and large-diameter circular knitting machine manufacturers. The prediction and quality control technology developed is expected to be adapted to other textile value chain processes in the future.

The new company is based in Argentona and draws on a number of patents for technologies which identify and show defects. Each of them can be purchased as a module which means manufacturers can tailor the solution to their knitting machines.

The project was nominated for the Innovation in Sustainability Award at the most recent ITMA, the leading trade fair for the textile industry held this June in Milan, and also picked up a prize at the Factories of the Future Awards 2023 in the category for Best Research and Development of Artificial Intelligence applied in industrial plants.

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