IntraVis SpotWatcher for online inspection of plastic containers for contamination and flaws

IntraVis Vision Systems of Germany have released the SpotWatcher – an automated vision inspection system that detects contamination in bottles on a production filling line.

SpotWatcher analyses the surface of plastic containers for pinholes, burn marks, streaks and other flaws. The SpotWatcher uses four cameras for simultaneous 360° inspection thereby reducing unnecessary handling. The high speed system can inspect (and reject) at a rate of up to 420 containers per minute without interrupting production.

The SpotWatcher has been designed to easily switch between products, as individual product settings are stored in a database, and can be recalled at the touch of a button. Establishing the optimal settings for a new product is enhanced by an intuitive setup process, where the user is prompted step by step to mark surface areas or adjust critical values. A typical setup takes only a matter of minutes.

Complementary online inspection machinery available from IntraVis includes:-

·         LabelWatcher - Can identify errors with the position and identity of the label. It recognises with absolute precision wrinkles and bubbles protruding more than 0.75mm, and sorts out defective products including incorrect text & colour without interrupting production.

·         CrownWatcher – Ensures that plastic or metal closures / caps are applied to bottles perfectly. The system can process up to 1,200 objects per minute. It identifies wrong closures / identity (colour and print), damaged or missing tamper evident band, crooked closure and bullnose or domed tops.

IntraVis are a global supplier of video inspection systems for monitoring quality in the Plastics and Packaging Industries, and have over 1600 systems installed in over 24 countries. The Vision machines are designed to perform in-line inspection of plastic, glass & metal packaging and components and to find and eject defects ranging from the shape, colour, contamination, or dimensional variances.