Carrying out several non-destructive quality control operations at any one time during production can optimise the production process and prevent non-conformities, which ensures complete product quality.
Robo-QCS, the automatic robotic system developed by FT System – part of Antares Vision Group – was developed to achieve these objectives. Let’s analyse its full potential in detail.
Non-destructive inspections can be carried out simultaneously in a single analysis platform. Usually, four types of controls or measurements are identified:
How Robo-QCS can do these inspections simultaneously
A robotic arm picks up samples from the production line and inspects them in an automatic analysis station through non-destructive tests.
These controls are required by law or necessary to optimise and satisfy internal processes and procedures. Compliant samples are automatically reinserted in line. It is not necessary to change the production speed of the line and no operator intervention is needed.
Line efficiency is improved by Robo-QCS
The inspection and quality control on the samples are performed in real-time in the automatic analysis station next to the production line, and this replaces the manual checks carried out on the line. The data is automatically time-stamped and correlated with the efficiency of the filler and the correct operation of the capper.
It is therefore possible to recalibrate the upper packaging equipment instantly in the event of deviation from specifications, monitor trends over time and plan targeted and punctual preventive maintenance.
Benefits that can be achieved with Robo-QCS
Controls of the Robo-QCS allow:
Integration with smart data management software
All the data of Robo-QCS can be uploaded by Avionics, a solution to monitor and manage data to maximise quality, performance, and maintenance of the bottling line. Avionics can collect and/or upload data from inline and offline machines or instruments, analyse and correlate them to facilitate making decision process about:
Avionics facilitates the root cause analysis and is supported by AI (artificial intelligence) and ML (machine learning) engines to implement preventive and predictive activities.
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