One of ERNI’s clients, a business leader in the field of sensor technology and software for manufacturing processes (MES), asked us for assistance with designing suitable software architecture that will meet the high demands of the process requirements of their clients. Today we are meeting in Germany, at a company that already uses our client’s products.

plastic reserach meets production
Overview of parts in the production hall

We are at an institute that is well known for its extensive research and experiments focusing on improving the processes of plastic manufacturing. It is the biggest player in the field of plastic research in Germany and was founded in the 1960s as a part of the local university. We are here to learn if the latest developments in the MES product have improved the institute’s benefit of it. Manufacturing plastic is a very complicated process that requires up to 2,000 parameters to be set up correctly in the moulding machine. Errors in doing so may result in anything from faulty products to damaging the production machine.

What advantages does the institute see in using our client’s MES system?

The biggest win is that data is acquired automatically from the very first step until the end of the production process, making the monitoring of the process very easy. The data is linked together, which allows analysing the process in much greater detail. Another advantage is that the software can process the input data of a wide variety of machines – and the institute owns many different brands and versions necessary for the experiments. We are lucky to experience the moulding process live at the institute and monitor the process on the MES system. First of all, the raw material is loaded into the machine.

raw material
Raw material/Granulated plastic

1. Input of the raw material

Then the machine is heated up in order to melt the granulate. Before starting the melting process, all parameters of the machine have to be set correctly: They depend on the raw material used, the desired product characteristics and the tool (mould) used. Traditionally, setting up these parameters has required expertise and experience of the researcher operating the machine. But thanks to the data gathered from past processes, the machine learning algorithms can support this step.

input of the raw material
Input of the raw material

2. The assistants run the machine directly through the machine’s SPS

In the institute, this process is monitored closely by the researchers in order to make sure the machine set up is working flawlessly.

running the moulding machine
Running the moulding machine

3. The process is monitored by the researchers

In our example, data is gathered from two different sources during the moulding process. On one hand, the machine gives us information about the status of the machine (e.g. current production mode, temperature of the machine, etc.). On the other hand, we have information from the sensors that are directly built into the tool (mould). They measure process characteristics (e.g. current pressure in the tool) in rapid intervals. We observe that the MES system of the new version is synchronising these data sources correctly, which is a difficult process as the data sources are completely independent.

monitoring the moulding process
Monitoring the process

4. The moulding plates are fitted with sensors to allow data acquisition within the process

These combined data enable the researchers to check if the produced parts fulfil their quality expectations. If not, the process parameters need to be adjusted.

fitting of moulding plates with sensors
Fitting of moulding plates with sensors

5. The end product can have all kinds of forms

All data from all the connected machines and devices (which can number in the several hundred) can be seen in the MES. With the help of smart algorithms, a deeper analysis can be made regarding the process, plant efficiency or other metrics. These in return can then help the institute find better production methods and reduce plastic waste, which then can help to declutter our seas of plastic garbage.

end product
The end product

Our client was able to analyse their MES product and identify individual areas of work. Together we defined a new system that allows smooth integration of multiple data sources and their synchronisation and has moved the processes towards a lightweight microservice architecture. These improvements allow the researchers at the institute to work efficiently with a wide variety of machines, analyse the data synchronised over the whole process and improve the industry’s manufacturing processes – with regard to reducing our ecological footprint. Smart Manufacturing is evolving extremely fast, especially in Germany. Our client confirms that the demand for fully integrated systems is growing steadily.

Analysing the end product

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