Quality plays a decisive role in insulating glass production. Customers rely on the properties of the products remaining the same and tolerances defined in various standards being adhered to. Production companies can only meet these requirements if they monitor the conditions under which the products are manufactured and take action if necessary. If quality defects occur, it is particularly important to be able to trace how they came about in order to define the necessary measures. With lis.qualitypass, LiSEC provides its customers with a tool that significantly increases traceability in insulating glass production.
The data that lis.qualitypass works with is provided directly by the machines and the production planning system, collected by lis.edgebox and transferred to the Microsoft Azure Cloud. There, this data is processed accordingly, pre-calculated and stored in databases for lis.qualitypass. We call this combination of machine data interface, production planning system, edge box and cloud infrastructure the LiSEC IoT platform.
The basis for data-supported decisions is reliable data and information. LiSEC provides its customers with an IoT platform that is based on four pillars:
Data is generated at various points in the production of insulating glass. On the one hand, the machine itself records measurement data such as pressures, temperatures and times. On the other hand, the production planning system provides information on the structure of the insulating glass.
The lis.edgebox collects this data from the machines and the production planning system and transfers it to the Microsoft Azure Cloud for further processing. The data is aggregated and correlated in the cloud environment. In addition, manual tests can be digitally recorded, managed and linked to the recorded data.
To be able to use lis.qualitypass, the following requirements must be met:
lis.qualitypass increases transparency in production so that quality defects can be recognized and linked to influencing factors. Potential for improvement can thus be identified and production control made easier. Automatically recorded measurement data and manually recorded quality inspections serve as the basis.