At ifm prover, different components for pressure sensors are produced on an automated production plant. At the end of the production process, the good and bad parts are separated.
For sorting, a gripper arm is used. For the purpose of quality control, the quantitative distribution is to be determined to enable analyses for assessing product quality and production processes.
The quantity produced was not centrally monitored and the volume of good and bad parts was not recorded. Production staff was not informed about the presence of bad parts and no overview of the scrap produced existed. The entire process was lacking transparency. An optimisation based on planning scenarios was not possible due to the lack of history data.
Introduction of a consistent quality assurance procedure through counting and analysis of the good and bad parts produced and optimisation of the manufacturing and downstream process (e.g. repairing of bad parts).
At ifm prover gmbh, moneo RTM is centrally installed on a server. The parts produced are sorted and separated into good/bad parts using two chutes. A photoelectric sensor was installed on each of the chutes to count the number of parts transported by the conveyor.
The multifunction IO-Link display shows both counters. It transmits them as an IO-Link signal to the IO-Link master. The IO-Link master provides the process data for visualisation, calculation and analysis in moneo RTM.
An effective quality assurance process has been implemented, leading to an improved process and product quality. Downstream repairs have been optimised. It is now possible to react promptly to changing process values. The measures have already proven to be cost-effective.
Thanks to the changeover, all objectives were achieved.
Get the big picture on the moneo dashboard. The dashboard provides the user with an overview of the relevant process values for this plant.
This function enables detailed analyses of the current state compared to recorded historical data. This makes it possible to identify production and quality processes, initiate appropriate measures and track their effectiveness.
Depending on the production process, quality fluctuations may occur due to changed environmental conditions such as room temperature or air humidity. By including further process values in the analysis, such correlations can be detected.
By setting individual thresholds, different alarm and escalation levels as well as information channels can be defined.
Definition of warning and alarm rules via the integrated wizard
CTU BAD+CTU GOOD= CTU TOTAL
Ratio of good parts to bad parts in %
Calculation of the ratio of bad parts compared to the total quantity.
RATIO GOOD BAD [%] = CTU BAD * 100 / CTU TOTAL