Beckhoff E Learning
Shari Alvine
Artificial intelligence (AI) is a very successful method of automating processes, as it imitates the cognitive abilities of humans. There are many different ways to imitate these skills, with the most successful approach in recent years being machine learning (ML), which is based on processing large sample data sets. The basic idea of machine learning is to learn solutions for specific tasks using representative example data.
The use of AI methods is particularly promising when the variance of the incoming data is difficult to predict. If, for example, the quality of wooden boards or agricultural/biological products is to be evaluated visually, the large variance of these natural products must be included in the evaluation. AI methods are highly promising and can therefore often be found in applications. The idea is that the solution to a task is learned from examples rather than applying a static sequence of algorithms to a previously devised solution.
Application examples from the field of image processing are always easy to grasp. This approach can also be applied to time series and other data. If a product is manufactured in a machine, AI can be used to process any metrologically recorded data points that describe the product in a representative manner (a kind of fingerprint of the manufactured product). This data can then be used to predict estimates or classifications.
The potential applications are incredibly diverse and cannot be described in full here. The McKinsey market research institute, among other entities, has singled out the following as the top fields of application for AI in the industry:
Beckhoff offers an open system workflow without a lock-in effect for the entire cycle, from data collection and model training through to the execution of the learned model in a productive environment. With its open system, Beckhoff makes it possible to meet specific requirements using toolboxes and functions from the TwinCAT modular system. This also applies to existing system infrastructures that are not based on Beckhoff products.
Each application and also each IT infrastructure places different demands on the method of collecting machine data: SQL or noSQL, file-based, local or remote, limited port releases, cloud-based data lake, and many more. A variety of established TwinCAT products are available for all of these scenarios, including the TF6420TwinCAT 3 Database Server, the TF3300TwinCAT 3 Scope Server, the TF3500TwinCAT 3 Analytics Logger, and the TF6720TwinCAT 3 IoT Data Agent, to name just a few. For image data, TwinCAT Vision even provides an entire product family for image acquisition, image (pre)processing, and image storage.
The concept of machine learning is based on learning relationships using representative examples. This is why it is essential to review and tidy up the recorded data to create a clean data set. As a general rule (with the exception of applications such as anomaly detection), the sample data must be annotated (labeled) before model training can be carried out successfully.
The TE3850TwinCAT 3 Machine Learning Creator allows even non-AI experts to develop efficient and high-quality AI applications. The software automatically creates AI models based on data sets. These AI models can be optimized in terms of their accuracy and latency to ensure they run efficiently on Beckhoff Industrial PCs with TwinCAT products. The generated models can also still be used as standardized ONNX models beyond the Beckhoff product range. For use with TwinCAT products, a PLCopen XML with IEC 61131-3 code is created in addition to the ONNX file, which describes the complete AI pipeline and can be imported seamlessly into TwinCAT.
The TE3850TwinCAT 3 Machine Learning Creator is also suitable for AI experts to accelerate and standardize the AI development process. The open ONNX export allows experts to read the created AI model into any framework for analysis or refinement.
AI experts can also train AI models completely without the TwinCAT 3 Machine Learning Creator. The training can take place in frameworks such as PyTorch, TensorFlow, and SciKit-Learn. Ultimately, only the ONNX export functionality of the framework is required. This means that there are no limits to AI model development.
If a trained AI model is available in the form of an ONNX file, Beckhoff offers the option of loading and executing these models on the control computer in the PLC. This makes the AI application part of the control application. The advantages of this integration are as follows:
A wide variety of different AI models are now available on the market. Some can be executed very efficiently even with small computing resources, while others require larger computing capacities in order to obtain a result within an adequate time. Beckhoff offers a flexible and scalable portfolio to suit all requirements.
For a large number of applications, the CPU-based execution of AI models is entirely sufficient. For these cases, a suitable hardware platform can be selected from the scalable portfolio of Beckhoff Industrial PC and Embedded PC components that meets the requirements for execution speed (latency).
In some cases, it is necessary to use hardware accelerators due to the achievable execution speed. With the C6043 ultra-compact Industrial PC, Beckhoff offers a scalable solution including an NVIDIA GPU. Both the Intel CoreTM-i CPU and the NVIDIA GPU can be selected for this device, resulting in cost-efficient solutions in this segment, too.
In the discrete production of metallic workpieces, the geometric shape is often a key quality feature. In addition to metric measurement methods to assess a workpiece quantitatively, qualitative statements (such as the classic categorization into OK and non-OK) are often sufficient.
Automation in the food industry contributes to the efficient and resource-saving supply of a wide variety of foods. One challenge is the automated sorting of foodstuffs, as these have a high natural variance compared to artificially produced products. In the context of eggs, for example, these should automatically be sorted into the categories OK, dirty, and broken. For this purpose, 200 images were taken with these three classes and annotated. With the TE3850 TwinCAT 3 Machine Learning Creator, it was possible to create an AI model that can correctly classify an egg in more than 90% of the cases considered. Using the explainability methods for AI models included in the product, it was easy to find out that misclassifications occurred especially in marginal areas from OK to dirty. This made it immediately clear what measures needed to be taken to improve the model: Either provide more sample data in the boundary area between OK and dirty, or define the boundary more cleanly by revising the existing annotations.
Wind turbines are a key component in the transition to renewable energies. They supply clean, electrical energy, which they obtain from the kinetic energy of the wind. Knowing both the wind direction and the wind speed is crucial for the efficiency of the system. The rotor attached to the nacelle is aligned with the wind direction according to the wind direction. As for the pitch of the rotor blades, this is adjusted according to the wind speed so that the turbine is operated as constantly as possible at its rated output.
Wind direction tracking and pitch adjustment are relatively slow, which means that the future wind direction and speed have to be estimated in order to move the turbine predictively to the optimum orientation.
Based on wind data collected from real wind turbines, an AI model was created that is able to estimate wind direction and wind speed values 10 to 20 seconds in the future with an acceptable margin of error. This is based entirely on past wind values. The created model can be easily integrated into TwinCAT with the TF3810 TwinCAT 3 Neural Network Inference Engine.
A mechanical bolt anchor essentially comprises the bolt, a washer, a hexagon nut, and a metal sleeve. The frictional forces between the sleeve and the wall of the drill hole ensure sufficient adhesion during use. To apply the normal forces required for the holding force to the drill hole, the sleeve is expanded with the drill hole via the conical head of the metal bolt.
Until this point, the quality of the sleeve around the bolt was mostly checked manually using a test gauge. Now, however, it has been demonstrated that each enclosure can be classified into three different categories (under-enclosed, acceptable, over-enclosed) within the quality specifications. The geometric key data of the enclosed sleeve (sleeve width, height, and opening) should also be predicted with a regression. The 100% inspection of the enclosure process is designed to detect trends or deviations at an early stage.
Instant noodles can be found in just about every food store in China. In a bid to reduce the number of products with packaging errors and the associated customer complaints, a large Chinese producer of instant noodles decided to turn to Beckhoff control technology including TwinCAT Machine Learning. This made it possible to perform intelligent and reliable real-time inspection of the packaging quality.
The TwinCAT 3 Machine Learning Creator automatically creates AI models based on data sets. These AI models can be optimized in terms of their accuracy and latency to ensure they run efficiently on Beckhoff Industrial PCs with TwinCAT products. The generated models can also still be used as standardized ONNX models beyond the Beckhoff product range. For use with TwinCAT products, a PLCopen XML with IEC 61131-3 code is created in addition to the ONNX file, which describes the complete AI pipeline and can be imported seamlessly into TwinCAT.
The TwinCAT 3 Machine Learning Server includes a connection to a local client as standard (local TwinCAT runtime). If (possibly further) TwinCAT runtimes need remote access to a TwinCAT 3 Machine Learning Server, these runtimes must each be equipped with a license for the TF3830 TwinCAT 3 Machine Learning Client.
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