Thanks for visiting Scientific Computing World.

You're trying to access an editorial feature that is only available to logged in, registered users of Scientific Computing World. Registering is completely free, so why not sign up with us?

By registering, as well as being able to browse all content on the site without further interruption, you'll also have the option to receive our magazine (multiple times a year) and our email newsletters.

SenseLink QM version 2.2

Share this on social media:

MKS Instruments has released the SenseLink QM version 2.2 data acquisition system, featuring multivariate analysis (MVA) with partial least squares (PLS) modelling for improved real-time quality monitoring and fault detection in injection moulding. The multivariate statistical analysis provides improvements in process monitoring and parts quality predictions that are not attainable through traditional SCADA and SPC approaches.

A compact, easy to mount unit, the SenseLink QM 2.2 system enables users to track multiple critical quality parameters in real time, to identify product variability and defects, and quickly determine root causes, improving part quality and machine productivity. It features multivariate libraries and automated modelling functions from Umetrics, a subsidiary of MKS. The SenseLink system incorporates the entire analytical process, including data collection, modelling and run-time control, in a single platform.

A web browser user interface allows easy configuration and data analysis. Setup does not require MVA training, and models can adapt to acceptable process changes due to time, shift change and material variability. Results can be automatically archived on local network storage servers for data retention. The system easily identifies process variables contributing to poor quality, allowing for corrective action to optimise production and eliminate downstream costs. Typical savings in inspection costs of 30 per cent have been reported by injection moulding companies using SenseLink QM 2.2.

Company: