Innovations boost Evolution NEO equipment sales

IRIS Inspection machines reports 154 planned and completed installations to date

The Evolution NEO series of smart inspection machines has received widespread acceptance from the international glass container industry since its official launch at the Glasstec 2018 exhibition last October.

IRIS Inspection machines reports 154 planned and completed installations to date, assisting the non-contact, automatic inspection specialist to gain new customers and market share.

Evolution NEO’s ability to discriminate intelligently between saleable and non-saleable containers has been widely appreciated, together with its simplified adjustment procedures and reproducibility features. In addition, glassmakers benefit from the equipment’s Industry 4.0 readiness, with the availability of intelligent data for process improvements.
The adopted methodology embraces defect identification, as well as the creation of statistics by defect type. Local trend analyses are produced on the machine, with information presented in a user-friendly format.

The equipment delivers valuable features that help glass container producers to save time during the manufacturing process. Every setting has been designed to be handled by the machine itself, making the equipment less dependent on human operators. Evolution NEO recognises the article and its exact shape, automatically drawing the inspection zone. This simplifies job changes and reduces the human error factor.

The equipment allows operators to follow defect rejection rates, while also bringing their immediate attention to the most significant information analysed by the machine. In addition, within its statistical tools, Evolution NEO integrates a helpful set of different data, including time, mould number, images, etc.

The latest IRIS software release improves inter-operability between Evolution NEO machines and hot end equipment, with the ability to share defect characteristics and defect images in real-time, alerting IS machine operators to instances of critical defect detection.