At the ITMA 2023 in Milan, from June 8–14, members of the Swedish Textile Machinery Association (TMAS) will present their fully integrated technologies with cutting-edge automated features for the end-to-end manufacturing of fully finished apparel and home textiles.
At the final ITMA show of 2019, for instance, ACG Kinna at stand C108 Hall 9 attracted sizable crowds to demonstrations of its robotic pillow filling technology in collaboration with a number of different businesses. Automated units cover the entire process, from the opening and weighing of the fibre to the filling of the product to the stitching and packing procedures. Each eight-hour shift can fill and finish about 3,840 pillows. The system has undergone additional development to add new components, such as an integrated A new software for automatically identifying pre-programmed problems will be deployed in Milan, along with a marking solution that enables the customer to print QR codes, batch numbers, and dates on the labels of the pillows.
Therese Premler-Andersson, the secretary general of TMAS, noted that technologies like artificial intelligence (AI), machine learning, and automation are becoming more and more significant in the textile industry. She also noted that in Milan, Swedish companies will present new equipment and software that can help improve efficiency and streamline production. Premler-Andersson stated that the AI and advanced automation already being used in a variety of ways by TMAS members like ACG Kinna and Eton has the potential to revolutionise the textile industry, improving production efficiency, quality control, and design processes. She went on to say: “AI-powered automation is already being used in a variety of ways by TMAS members like ACG Kinna and Eton. Systems can, for example, help detect defects in fabrics and garments during manufacturing processes. By using computer vision in the machinery, different defects such as stains, holes and uneven stitching can be rapidly identified and corrected at an early stage. Predictive maintenance is another benefit. AI is being used to monitor machines and predict when they are likely to need maintenance. This can help prevent breakdowns and reduce downtime, improving overall efficiency. AI is also proving valuable in R&D for TMAS companies, enabling data from different sources to be coordinated in order to optimise product design and reduce time and costs via the sensor-controlled optimisation of a host of different parameters.”