MPS 400 Sorting Inline System station

MPS 400 Sorting Inline System station


Brief description
The MPS 400 Sorting Inline system station covers a number of topics including differentiating between different workpieces through combining a variety of sensor types. The use of algorithms from the field of machine learning in small computers equipped with a camera provides students with a simple introduction to the practical application of artificial intelligence in production. At the same time, students acquire a good understanding of the advantages and challenges of retrofitting existing systems with IIoT and the related development of potential new business models

An RFID reading/writing device with height adjustment adapts to incoming workpieces and reads their product memory. Afterwards, the color and material of the workpiece are detected. By comparing the measured values with data from RFID or a connected MES, errors in the color and material of the workpiece can be detected. The workpieces are then either sorted onto one of two slides or passed on to downstream stations. Using a camera, an IIoT device monitors the two slides. A machine learning algorithm on a small computer analyzes the images of the camera and in doing so detects the number of workpieces on each slide.

Sensor combinations
Students explore the combination and evaluation of different types of sensors, in this case diffuse sensors, light barriers and inductive sensors. In this way, they recognize how the combined use of sensors can provide information that no sensor could capture individually.

Machine learning
Students gain an easy introduction to the complex field of artificial intelligence and its practical application in the production environment. The advantages and disadvantages as well as the typical steps and challenges involved in retrofitting existing production facilities (IIoT retrofitting) can also be conveyed here. The additionally obtained data within the scope of IIoT retrofitting increase the quality of the decisions made. These improvements gained through machine learning also demonstrate the opportunities for new business models.


  • The extensive modularity makes it possible to use the MPS 400 system modules within the context of a wide range of project work.
  • An MPS 400 system station can be used individually as well as in combination with other MPS 400 system modules as a system network.
  • Comprehensive training documentation breaks down complex topics into small steps and learning units, allowing teaching to be designed in a structured yet flexible manner.
  • The learning process is supported by modern media such as QR codes and AR for provision of information as well as interaction with the learning system based on augmented reality.

Technical data

  • Operating pressure: 600 kPa (6 bar)
  • Power supply: 110/230 V / 50/60 Hz
  • Square/round workpiece dimensions: max. 40 mm
  • Dimensions (W x D x H): approx. 350 – 700 x 700 x approx. 1705 mm (variable height)

Training content

  • Detecting different workpieces through the combination of different sensor types
  • IIOT retrofitting of existing industrial systems
  • Practical application of artificial intelligence (AI) and machine learning (ML) in production
  • AI/ML supported evaluation of camera images in an automated environment
  • Development of new business models through IIoT retrofitting