The Swedish Textile Machinery Association (TMAS) members will demonstrate their fully integrated technologies with up-to-the-minute automated features for the end-to-end production of fully-finished garments and home textiles at the ITMA 2023 in Milan on June 8-14.
In partnership with a number of other companies, for example, ACG Kinna at stand C108 Hall 9, drew considerable crowds to demonstrations of its robotic pillow filling system at the last ITMA show in 2019. With the ability to fill and finish some 3,840 pillows per eight-hour shift, automated units cover the entire process โ from the opening and weighing of the fibre to the filling of the product and on to the sewing and packing processes. The system has been further developed to include new features including an integrated marking solution which allows the customer to print QR codes, batch numbers and dates on the pillowsโ labels, and a unique software for automatically detecting pre-programmed faults which will be introduced in Milan.
Swedish textile machinery increases efficiency with automation
TMAS Secretary General Therese Premler-Andersson said that technologies such as artificial intelligence (AI), machine learning and automation are becoming increasingly important in the textile industry and stated that in Milan, Swedish companies will showcase new machines and software that can help streamline production and improve efficiency. Premler-Andersson relayed that the AI and advanced automation already being used in a number of ways by TMAS members such as ACG Kinna and Eton has the potential to revolutionise the textile industry, improving production efficiency, quality control and design processes and continued as follows:
โAI-powered 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.โ