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Cosmino Digital Twin for Methodical Acquisition of Process Characteristics

Digitalization is increasingly making it possible for process data to be available from machines and production lines. An analysis of this data promises optimization potential for the entire material flow if, for example, poor quality can be detected earlier and malfunctions can be avoided.

A meaningful concept for process data requires a system architecture designed for "Big Data" and the ability to generate semantics when storing the values. This is exactly what Cosmino MES and the Digital Twin offer:

Benefit 1: Powerful System Architecture Designed for Big Data

Even if only a two-digit number of different process characteristics is recorded for each production machine, this already generates an enormous amount of data. A relational database such as MSSQL or Oracle SQL is then not ideal for such mass data. Better suited are time series databases (TSDB) – these are so-called NoSQL databases – which can work much faster with large amounts of data. COSMINO AG relies on such time series databases for Big Data; in addition to performance advantages, these can often even be used free of charge. Whether a local database or a cloud application is used is insignificant and can be freely decided.

To prevent the process data from coming into contact with the relational database of the MES in the first place, the Cosmino Digital Twin is used to record and display the process characteristics on the shop floor. This Digital Twin of the machine takes over its process data and stores it temporarily. This means that MES applications on the production floor can access it to visualize values or check them against limits. The Digital Twin then passes the process data to the time series database.

Benefit 2: Semantics:

Without the semantic link to an order, product, machine, machine status, tool, part ID, limits, quality, etc., a recorded process characteristic lacks any utility for further analysis. Cosmino MES therefore generates the necessary semantics by linking the characteristic value with the operational data that is also recorded. In this way, meaningful analyses can be performed with the process parameters stored in the time series database.

Summary:

Process data acquisition needs MES and Digital Twins:

    1. Adoption of the machine data to the Digital Twins.
    2. Enriching the process data with other relevant information, e.g. product, machine status, quality, etc., which are recorded with other MES functions.
    3. Possibility to display the recorded characteristic values including semantics in the shop floor dialog of the MES.
    4. Possibility of comparing the recorded values with the limit values from the MES and, if necessary, triggering an alarm. This is currently not yet implemented.
    5. Possibility to transfer values, outliers or average values for characteristics required in the MES to the MES database (e.g. for evaluations, traceability ...).
    6. Transfer of all recorded process characteristics to a time series database that is optimized for process data. The database can be installed locally or located in a cloud.
    7. Access and graphical display of process data in the time series database, e.g. via the open source application Grafana (available locally and in the cloud). Reporting cockpits can be prepared in the project by COSMINO AG report specialists.
    8. Data mining, AI-based data analyses, etc. with the acquired data (not included in the MES).


This constellation means that the acquisition functions of Cosmino MES and the analysis of process data outside Cosmino MES are consistently intertwined. Neither does data have to be acquired or stored twice, nor is semantic information lost.