The Eurecat technology center and the textile company Canmartex have created the spin-off Aracne, focused on predictive systems and quality control of the knitwear manufacturing process. It has a business plan that foresees the market launch of the first product in 2023 and a sales forecast of six million euros in 2025.
The Aracne system predicts deficiencies that occur in knitted fabrics with large-diameter circular machines caused by breakage or wear of machine components, such as needles or sinkers, in such a way that production defects can be reduced by more than 50%, thus promoting the circular economy.
The device monitors the output of the fabric when it is being manufactured to detect defects that occur at that moment, such as problems with the thread, mechanics or holes in the fabric. “The difference with other technologies is that Aracne anticipates defects” thanks to photonic systems combined with artificial intelligence algorithms”, underlines the director of the industrial area of ??Eurecat and director of technology of the spin-off, Xavier Plantà.
The general director of Aracne, Enric Martí, advances that “the ability to predict defects allows the production manager to be informed” so that he can make decisions, so it is possible “to anticipate before deficiencies occur” and correct the causes that cause them.
The innovation has been developed specifically for large diameter circular knitting machines, in collaboration with textile machinery manufacturer Canmartex. “It helps to increase production and reduce costs” adds the researcher from the Eurecat advanced manufacturing systems unit, Josep Maria Serres. In the future it can also be adapted to other processes in the textile chain.
It is estimated that 92 million tons of textile waste are generated annually, of which 25% is produced during the manufacture of the fabric. Thus, the Aracne solution allows real-time analysis of the state of degradation of the most critical components in the manufacturing process and predicts the most common defects, such as holes, scratches or stains that are detected after the manufacturing process. The project has been nominated for the innovation in sustainability award in the latest edition of ITMA, the benchmark sector fair, and has won the Factories of the Future Awards in the category of Research and Development of Artificial Intelligence Applied to Industrial Plants.
