PROCESSING | MEDICAL
IMAGE: NETSTAL
are recorded and monitored in real time via the Mouldflo water distributors integrated into the mould mounting platen. Other partners involved in the exhibit are Petek
(cleanroom technology), HB Therm (temperature control units), Piovan (material feed), Sabic (mate- rial), Sax Polymers (masterbatch) and BFA solutions (MES).
Above: Netstal will use a new Elion 1200 Med to produce a housing for auto-injectors at K2025
compact design and shorter length by optimising the geometry of the toggle lever – for faster drying cycles – and adding a new injection gearbox with a compact metering drive. The reduced lengths have not affected clamping force – while the clamping unit area also offers better accessibility for mainte- nance work, it says. The whole Elion portfolio is being updated over
Right:
Sumitomo Demag says it is too early to apply AI to validated processes like medical moulding
time, but for now four variants with clamping forces of 1200 and 1750kN are available. The target portfolio will cover 800 to 4200kN. The newly designed toggle lever with ‘double connecting rod’ distributes the closing force over two bearing points and has been separated from the axis for the electric ejector. At the same time, Netstal says it has trimmed all drive components such as motors and gearboxes for maximum performance – allowing the Elion Med to maintain high speeds at high pressure in continuous operation. To raise energy efficiency, Netstal has designed a new type of direct drive for the unit movement and the application of the nozzle contact pressure. At K, Netstal will produce a housing for autoin- jectors in a cycle time of 12 seconds. The machine, equipped for use in cleanrooms, has a clamping force of 1200kN and uses a four-cavity mould from Zahoransky. The mould features individual, servo- electrically controlled valve gate nozzles and has a rotary unit with an integrated servo motor. Innova- tive index technology helps to cut the cycle time compared to conventional mould concepts, it says. A six-axis robot mounted on the injection moulding machine will remove finished housing parts with the mould closed. The compact automa- tion solution from Saxe includes optical quality control and a production-ready solution for automatically changing the containers. The handling system is also fully integrated into the Axos control system of the moulding machine. Thermal parameters such as flow and return temperatures and the water flow rate in the mould
12 INJECTION WORLD | October 2025
AI in medical? Machine learning (ML) can help manufacturers improve a wide range of production situations – but is not yet suitable for strictly validated process- es like medical moulding, says Sumitomo (SHI) Demag. “One of the biggest obstacles to ML in medical manufacturing is the altering of processing parameters,” said Andreas Montag, medical business development director at the company. This is currently referred to as ‘Black Box ML’. If
the processing algorithm is changed autonomously – and it is not clear how a machine arrived at this decision – it is hard to assign accountability, he says. “Although the risk might be minimal, failure to
detect processing errors may cause harm to end users, resulting in a legal investigation,” he added. In medical manufacturing, robust validation
protocols are critical throughout the product lifecycle, including any changes that could impact product quality. In accordance with ISO 13485 standards, documentation of the validated injection moulding process must be maintained. When machine settings are adjusted to allow for changes in the environment or to address moulding issues, this will trigger a re-validation exercise – as would repairs, changes, or improvements to the mould tool and machine. The cost and time implications of
IMAGE: SUMITOMO DEMAG
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