Industry and Universities

fischertechnik reference projects
simulation models

Our simulation models are already being used and applied in many companies and universities for research as well as education and training. Here, some customers provide insights into their work with fischertechnik simulation models.



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Example showcase

Creating factory digital twins with MongoDB Atlas

Teaching and researching with fischertechnik

Quality control with AI

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Discover more reference projects with fischertechnik simulation models

Example showcase

Creating factory digital twins with MongoDB Atlas and fischertechnik Training Factory

The digitalization of the manufacturing industry has given rise to the development of smart factories. These advanced factories incorporate IoT sensors into their machinery and equipment, allowing workers to gather data-driven insights on their manufacturing processes. However, the evolution does not stop at smart factories automating and optimizing physical production.

The emergence of virtual factories introduces simulation capabilities and remote monitoring, leading to the creation of factory digital twins. By bridging the concepts of smart and virtual factories, manufacturers can unlock greater levels of efficiency, productivity, flexibility, and innovation. However, setting up a virtual factory for complex manufacturing is difficult. Challenges include managing system overload, handling vast amounts of data from physical factories, and creating accurate visualizations.


The virtual factory must also adapt to changes in the physical factory over time. Given these challenges, having a data platform that can contextualize all the data coming in from the physical factory and then feed that to the virtual factory and vice versa is crucial. And this is what MongoDB Atlas enables. 

MongoDB Atlas is the leading multi-cloud developer data platform for building modern applications. It can provide synchronization capabilities between physical and virtual worlds, enabling flexible data modeling and providing access to the data via a unified query interface. Please see the video below to understand how the MongoDB team leverages the Fischertechnik training factory to build a real time virtual factory/digital twin.

 

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Video Referenz Lernfabrik
Video Referenz Lernfabrik
Video Referenz Lernfabrik
Example showcase
Creating factory digital twins with MongoDB Atlas and fischertechnik Training Factory 

Teaching and researching with fischertechnik

„The interaction of software and haptics creates understanding for machine vision“ - Prof. Dr. Carsten Müller

The laboratory and competence center for "swarm-based logistics" of the Cooperative State University in Mosbach / Campus Bad Mergentheim focuses on research in the context of artificial intelligence with an emphasis on swarm intelligence and deep learning. The Bad Mergentheim campus is part of the Cooperative State University in Mosbach and focuses on teaching and research on artificial intelligence. Prof. Carsten Müller researches the application of swarm intelligence, in particular the adaptation of nature-inspired algorithms to application areas in logistics. In his research and teaching on artificial intelligence with a focus on machine vision, the powerful technology from fischertechnik "Quality Control with AI" is used.  

If in the human body it is nerve cells and their interconnections that are referred to as neuronal, the same applies to artificial neuronal networks in the subject area of Deep Learning. Neural networks can take in information from outside and pass it on modified to other neurons and output the classification as the final result. "And they can be trained," says Prof. Dr. Carsten Müller, describing the flexible learning capacity of neuronal structures. Machine learning is similar to human learning in that (positive as well as negative) reinforcement achieves adaptations in the neural structures that enable the system to develop strategies on its own. "In particular, the power of reinforcement learning is fascinating," he adds. 

Artificial intelligence technologies have been used in everyday life for some time. For example, navigation systems, image recognition or voice control are often based on AI. Artificial intelligence is also increasingly regarded as a key technology in industry, trade or commerce. In automatic process control in manufacturing, for example, artificial intelligence is increasingly being used in conjunction with camera systems. Workpieces or products are intelligently further processed here by means of supervised learning. Supervised learning is a subarea of machine learning. It occurs when a system not only processes data, but also recognizes patterns and derives decisions from them.

Prof. Carsten Müller is researching the application of swarm intelligence, in particular the adaptation of nature-inspired algorithms to application areas in logistics. The focus of the first phase in research and teaching is machine vision as a key technology for the stable classification of objects and situations in the context of autonomous driving. 

 
In the further phases, hybrid algorithms based on swarm intelligence and reinforcement learning will be integrated. Issues to be explored include areas such as skill distribution, dynamic roles and responsibilities, behavioral rules in different situations, and interaction between autonomous delivery robots as well as with humans. 
His research and teaching on artificial intelligence with a focus on machine vision will use fischertechnik's powerful "Quality Control with AI" technology. "The interaction of software and haptics creates understanding for artificial intelligence," says Carsten Müller, explaining his decision to use fischertechnik. 

The fischertechnik Quality Controle with AI comes with workpieces in different colors. These workpieces are marked with three machining characteristics as well as various defect images. They are scanned by the camera and classified and sorted using supervised learning - depending on color, feature and defect pattern.  


The AI used is implemented with machine learning in Tensorflow, where an artificial neural network was trained with image data. The learned AI is executed on the fischertechnik TXT 4.0 controller, which offers the appropriate wireless interfaces for numerous applications. The model's sequence control is implemented in the ROBO Pro Coding programming environment and in Python. 


In addition, it is possible to program own AI applications. The training is done by an algorithm based on Python, a universal higher programming language. An example project is provided for the possibility of training. 


In understanding the complex processes of supervised learning, it becomes clear how intelligent machines function in industry. "The fischertechnik model is powerful, smart and intuitive to use, making it ideal for teaching artificial intelligence," explains Carsten Müller.
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KI Tool Prof. Müller - Qualitätssicherung mit Künstlicher Intelligenz

More references

 

Use case fischertechnik for touchable demonstration of AI optimization tools.

In order to give interested parties at trade fairs or even customers a better understanding of the functionalities and benefits of the intelligent software tools Shannon® and Darwin, the company plus10 uses a demonstrator with fischertechnik factory simulation.

Accso - Accelerated Solutions GmbH - Anomaly detection

The company Accso - Accelerated Solutions GmbH used a fischertechnik assembly line model to simulate quality control in the production process as part of the project "Anomaly detection in pictures". 

Luleå University of Technology

In order to teach his students the principles and possibilities of Industry 4.0, Jan van Deventer, Associate Professor of Industrial Electronics at the Department of Computer Science, Electrical and Aerospace Engineering at the Technical University of Luleå in the north of Sweden, set up a production plant with doctoral student Aparajita Tripathy as an application example.

 

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pmOne Group - The fischertechnik training factory as a mature Industrial IoT testbed

The data experts at pmOne use the fischertechnik training factory as a full-blown IoT testbed, because in conjunction with the Microsoft Azure Cloud, they can test current technologies in the field of Industrial IoT and Edge Computing.

Light metal foundry of the BMW Group

In the light metal foundry of the BMW Group's Landshut plant, the start-up of the production facilities is to be made safer and more efficient with the help of two measures: Standardization and virtual commissioning. 

SmARt Factory - fischertechnik factory model virtually extended 

As part of a project, the company Accso GmbH enables the planning, analysis and visualization of production plants with the SmARt Factory and augmented reality applications.

From a 3D CAD model of the high-bay warehouse a digital twin was created - Furtwangen University

With the digital twin, it was shown how the development of a control program can be carried out via SiL (Software in the Loop) simulation without physically existing hardware, thus enabling virtual commissioning of the complete plant.

IoT simulation - Objektkultur Software GmbH

With the help of IoT simulation, Objektkultur Software GmbH was able to show how a factory equipped with IoT can be quickly and securely integrated into the existing IT and controlled in the cloud.

Pilz Education Systems PES - Pilz GmbH & Co. KG

The Pilz Education Systems PES are modular training systems with modern industrially used components for practical instruction in electrical engineering.

RISE Remote Support - RISE Technologies GmbH

RISE Technologies GmbH is a software service provider and regularly travels to major trade fairs to demonstrate its digital innovation RISE Remote Support and make it tangible for visitors.

Sorting line with colour recognition - Staatliche Technikakademie Weilburg

The students of the Staatliche Technikakademie Weilburg programmed an independent project with fischertechnik using LabView. They extended the part of the sorting line with colour recognition from the factory simulation and turned it into an independent project.

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