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More efficient commissioning through data analysis

IoT Use Case ITE SI, Manz
4 minutes Reading time
4 minutes Reading time

Commissioning in special equipment manufacturing for battery production is complex. Each machine is unique and is adapted to the requirements and production lines of the customer. With data, the process can be shortened because their evaluation can better analyze a machine.

The challenge: Commissioning is time-consuming

Production technology for lithium-ion cells is experiencing a boom in mechanical engineering, not least due to the expansion of electromobility. Reutlingen-based Manz AG is a specialist in special equipment and manufacturing technology for battery cells, battery management systems and other electronic components, mainly for e-mobility.

The production facilities are complex systems, which are planned, developed and commissioned in cooperation with the customers. The commissioning process, in which the plant is improved and adapted to the requirements of cell production, is also complex. Manufacturing is highly material dependent, so extensive testing with the material later used in production is necessary to meet all performance and quality criteria. This testing phase can be accelerated with data evaluations.

To improve the entire process chain and make it more efficient, Manz AG has connected the machines to the Industrial IoT (Internet of Things) for data evaluation. Collecting and analyzing the machine data should facilitate and shorten the testing phase. In addition, the data supports the users of the machine in productive operation in optimizing production as well as maintenance and repair.


The solution: Evaluate data with Edge and Cloud

The simple connection of machines and plants to the Industrial IoT as well as the storage and evaluation of data is the focus of the “IIoT Building Blocks” from IT-Engineering Software Innovations (ITE-SI), a full-service development partner for software development in mechanical engineering. The blocks cover three areas:

  • Collect: The Data Collector networks machines and collects the data – even with heterogeneous systems on the shop floor, for example through interfaces to existing PLCs.
  • Explore: The data collected is visualized and evaluated in freely configurable dashboards.
  • Improve: Data-based predictions enable optimized use, higher quality, and new digital products and business models.

This enables Manz AG to simplify commissioning processes, search for errors, and improve its products and services with this data. Data sources are the PLCs as well as sensors and cameras. One special feature applies here: the measured values in industry often arrive at very short intervals of a few milliseconds. This creates very large amounts of data within a short period of time. Optical inspection of the object position or of weld seams also generates large amounts of data.

Neither should simply be moved to the cloud. The combination bandwidth and data storage requirements are too high. Since there is also a return channel for alarm messages or control commands, the response times would be too high. ITE-SI therefore uses edge servers that process and filter data on site. This reduces the amount of data that needs to be processed by the cloud platform and the respective applications (dashboards).

The result: Efficient commissioning and monitoring of ongoing production

Dashboards adapt to each employee’s role and function. The commissioning teams as well as the teams of Manz’s customers see all necessary information and can, for example, monitor the progress in performance and quality. This speeds up the test phase in particular: shortly after a test run, the relevant data is available and employees can correct the settings of the machines.

However, the data analyses are also important for ongoing production, both for the machine builder and the battery producers. Manz AG can use the analyses to improve its entire product portfolio, from individual components to complete systems. The customer companies receive help in optimizing production and maintenance through the analyses. This makes it possible to replace cyclical maintenance with predictive maintenance based on machine data and machine learning: Data analyses show when a malfunction is imminent. Maintenance without lengthy interruption of operation can now be planned.

In application

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