Skip to main content

Exemplary presentation - learning in scenarios

Learning scenarios as a supporting didactic element of the Application Center industry4.0 enable target group-oriented training and further education measures for all participants. In particular, the application and experience of practical scenarios are in the foreground. These address problems formulated in advance and thus encourage the participants to actively reflect. Particular attention will be paid to the continuous integration of IIoT technologies (AR glasses, tablets), which can also be found in real production environments of the future.

The exemplary process presentation "Smart quality assurance in logistics" illustrates the didactic advantages of the medium "learning scenario". It explains in detail both the practical integration of mobile IIoT technology into a learning factory and its benefits for the development of action competences. Semi-finished products are forwarded to subsequent departments on the basis of incoming picking orders and quality tests are carried out using mobile IIoT technology. Two people assume the roles of production manager (learner B) and technical quality manager (learner A). Lerner A is equipped with an intelligent glove and an intelligent watch, Lerner B with AR glasses and a tablet. Lerner A's task is to check the status of the semi-finished workpieces after the "powder coating" production step using the glove and the watch. The glove is to be used to determine essential quality parameters of the powder coating by means of integrated sensors. The measured values determined are stored in a central quality assurance system and checked for any limit value violations. If the QA parameters exceed the limit values, the smart clock provides visual and haptic feedback on the quality of the workpiece. In this case, the workpiece is ejected from the production process. If a similar deviation occurs repeatedly, the quality manager (Lerner A) can inform the production manager (Lerner B) directly, since the assumption of a system error is obvious. In extreme cases, Learner A can initiate a production stop. Learner B receives the message on his tablet with additional information (e.g. the affected machine) in order to be able to deal with the problem in a targeted manner. At the machine, he receives relevant machine parameters on his AR glasses. A comparison with the recommended reference values enables the cause to be analyzed and forms the basis for troubleshooting. The detailed analysis of the workpiece itself is supported by the AR glasses. There the user receives the measured parameters as well as suggestions for further action (e.g. reworking or scrapping). Based on the information and the detailed analysis, Lerner B makes the decision about the further procedure, as he is familiar with the process.