Lonati links digital twins, AI and ±1mm measurement for medical hosiery

Italian knitting machinery manufacturer Lonati laid out a fully connected route to medical compression hosiery manufacturing at an industry event in Albstadt, Germany on 7–8 October 2026, combining machine programming, digital product simulation, automated measurement and AI-driven production analysis into a single feedback loop.

The presentation came from Mattia Polimeni and Luca Viecchi at The Loop – Chapter One: Medical Textiles, held at Groz-Beckert's Albstadt site under the theme 'From Precision to Performance'. The tier it concerns is specific: medical compression hosiery producers running Lonati knitting equipment, a segment where dimensional accuracy and repeatability are compliance-critical rather than merely desirable.

What problems is Lonati targeting?

The company framed its pitch around pressures medical hosiery manufacturers already recognise. Product complexity is increasing, development cycles are shortening, and costs are rising — all while compression performance has to stay consistent.

Traditional development in this segment involves repeated physical sampling and machine adjustments. Lonati and its technology partners argue digitalisation can substantially cut both, with the company citing shorter time-to-market, fewer physical samples, reduced material waste and improved product consistency as the principal benefits.

How does Orion change programming?

Central to the approach is Orion, Lonati's integrated programming and design environment, which pairs knitting machine programming with three-dimensional product visualisation. Developers work directly with a digital representation of the knitted article, linking graphical design to machine instructions.

The distinction matters for sourcing teams evaluating the tool. Unlike conventional design software, Orion is built to show what the Lonati machine will actually produce, not an approximate visual representation. Users select machine configurations, develop knitted structures and modify stitch patterns and yarn arrangements through a single interface, with a library of predefined shapes and structures simplifying programming.

The 3D visualisation functions as an operational digital twin. Technicians can examine individual garment areas, assess structures and refine designs before anything reaches physical production — particularly relevant for compression hosiery, where different zones of a garment need carefully controlled structures and elastic properties to hit the intended compression profile.

Lonati also expects digital sampling to reduce yarn consumption and improve brand-manufacturer collaboration, since approvals can run against a shared digital reference rather than couriered physical samples.

What do the AI tools promise?

Two developments remain in progress, and buyers should treat them as roadmap items rather than shipping products.

Orion Predictive, an AI-assisted system, uses design parameters, yarn characteristics and target dimensions to predict appropriate knitting machine settings. Those optimised parameters would then transfer directly to the Lonati machine, cutting the number of adjustments needed to reach correct finished dimensions.

Arcadia Predictive and Intelligence, meanwhile, is designed to analyse machine behaviour, identify abnormal operating conditions and support predictive maintenance. The system would use historical and real-time production data to recognise patterns associated with machine faults, stoppages and reduced efficiency.

Where does measurement fit?

The measurement layer falls to DSC EVO, a new-generation system for hosiery, socks and underwear developed within the Lonati and Dinema technology ecosystem. It offers measurement accuracy of ±1mm — a concrete benchmark for a category where compression performance depends on dimensional control — and ships with a 9-inch touchscreen, integrated cameras, automated measurement functions and digital connectivity.

DSC EVO integrates with Orion, Arcadia and third-party software, meaning measurement data feeds directly into production and quality-control workflows rather than sitting in a standalone report.

What does Arcadia deliver on the knitting room floor?

Dinema's Arcadia platform collects production data directly from Lonati machines and delivers real-time monitoring, reporting and analysis. It records production quantities, machine status, operating conditions and stoppages, and presents them through dashboards that let operators and managers spot inefficiencies quickly.

Its modular architecture supports integration with external manufacturing management systems, and built-in business intelligence tools add a further layer of production analysis.

Together, Orion, Orion Predictive, Lonati machinery, DSC EVO and Arcadia form a connected chain linking design, prediction, production, measurement and analysis. Measurement results and production data loop back into subsequent design decisions and machine settings, incrementally tightening accuracy and efficiency.

What should sourcing and compliance teams take from this?

The hardware base is established: Lonati's portfolio includes the GE single-cylinder series, ETE double-cylinder series, LBE pantyhose series and LBEME medical series. What's new is the direction — digital twins, AI prediction and real-time production data moving from optional extras toward the default architecture of medical textile manufacturing.

For medical compression hosiery suppliers, the decision the news forces is sequencing. The confirmed, available components — Orion programming, DSC EVO measurement, Arcadia monitoring — can cut sampling rounds and waste now. The predictive layers are still under development, so capacity plans built on them should carry that caveat.