Find user stories of interesting ideas and solutions in this blog.
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A standard in IT for years, it has not yet made much of an impact in industry. Often such technologies are seen as
too complex and unnecessary. The question that arises is, do they bring us advantages?
A vision for PLCnext using the example of Kubernetes.
For a faster development we want to create ARM based containers for the AXCF2152 or AXCF1152 on x86 hardware. For this purpose, a virtual machine based on Debian or Ubuntu is used, which runs on our normal PC.
This demo project shows how to create a custom keypad for use with the eHMI in PLCnext Engineer. This was developed as a work-around for the BWP 2000 series HMI touch screens, but will work with the WP 4000 series touch screens as well.
Systems quickly become very large when you ship it complete. This includes the build and often the build and test environment. Even if the extraction of the files to be shipped is not a problem, it is time consuming.
Containers offer the possibility to do this automatically.
This little demo project shows how to easily use a PLCnext controller as a smart home device in the open-source home automation software Home Assistant via the REST API and Modbus TCP. With the PLCnext controller you can use the Axioline components in your automation to add easily more powerful Outputs then the GPIO Pins of the Raspberry Pi, temperature sensors like Pt100 and bus systems like DALI. All you need is a working Home Assistant installationand a PLCnext controller with a small but runnable PLCnext project.
By now everyone has heard about Machine Learning and how it will change everything. But very few have an idea how to start to change everything. I hope that when you try the steps in this blog, or even read about it will help you understand how to get started to change things with your PLCnext Controller. In this blog I go about training your first ML model, converting it to the ONNX standard and inferencing the model on a PLCnext controller. As to not go and make things to overwhelming I'll be using the famous Iris dataset to build our model.
Apache Kafka is a framework for data ingestion, storage, processing, and redistribution. Nowadays, it is widely deployed at companies all over the world. Kafka's official website offers more information about its idea and how to deploy it. One of its key features is the huge number of already existing connectors to other applications and communication protocols like MQTT.
Azure IoT Edge makes it possible to move cloud analytics and costum logic to to "the edge", in our case to our PLCnext device. This has some benefits in decreasing used bandwidth and latency. With Azure IoT Edge you can develop and deploy your own applications form a central cloud application. In this blog you will learn how to set Azure IoT Edge and some of it's basic principles.
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