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Laempe - Big Data meets foundry

How data analysis by the DIGITAL COCKPIT helps to optimize the core production process


The baseline situation: core production is subject to a large variety of influences

Especially in core production, there are situations in which apparently identical conditions produce different core qualities. This sub-process in the complex foundry process is subject to a large variety of influences. The main difficulty is that some of these influencing factors are still unknown and some of the known variables can not be directly influenced. If the cause of the error is not obvious or known, cause analysis and troubleshooting are usually based on the experience of the employees and are only partially structured. The documentation of the troubleshooting steps is often neglected, so that a reproducibility of cause, measure and result is rarely given. Core production therefore faces the task of making processes more transparent.

The digital solution: The DIGITAL COCKPIT brings data transparency 

The DIGITAL COCKPIT stands for data transparency, visualization and digitization in core production and has been developed together with pragmatic industries GmbH, a startup in which Laempe Mössner Sinto GmbH participates. The visualization software can be used to display the most important key figures in core production, such as machine parameters, productivity, standstill detection, energy consumption and much more. By monitoring as many parameters as possible, parameter changes that can adversely affect the overall process of core production can be determined. Due to the DIGITAL COCKPIT's permanent analysis of the system data, the overall equipment effectiveness (OEE) can be permanently calculated and is broken down into quality, machine performance and availability.

Laempe Mössner Sinto will present the DIGITAL COCKPIT at GIFA (Hall 17 Stand B40).

Advantages of DIGITAL COCKPIT:

The Monitoring System enables 100% data transparency of all machines and systems.

Ongoing analysis optimizes OEE related processes.

Monitoring of machine parameters allows for observation and reduction of resource consumption.

Detailed real-time monitoring reveals productivity potential.

Process monitoring is the basis for the long-term reduction of costs per core.

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