24-month Festo Didactic warranty → www.festo-didactic.com 70 71 Learning factory kit > Single workpiece flow > MPS 400 Stations 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 Process 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. MPS 400 Sorting Inline Sensor Combinatorics and Machine Learning Learning factory kit > Single workpiece flow > MPS 400 Stations MPS 400 Sorting Inline 8129438 Essential components: MPS Station Sorting Inline with one Conveyor belt module and two Slide modules Module detection with diffuse sensor, light barrier and inductive sensor IIoT retrofitting module with camera and machine learning algorithm 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. 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 Recommended learning material Courseware Complete overview → Page 270 For example: eLearning courses – Introduction to Industry 4.0 – PLC Programming eTheory courses – CIROS – First steps eLab courses – Basics of PLC Programming – PLC Programming for Smart Systems – CIROS – Basics of 3D Simulation Evaluations – Basics of PLC Programming – PLC Programming for Smart Systems User Guides – CIROS – Installation Instructions
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