The term „Industry 4.0“ or „Industrial Internet of Things“ describes the continuing trend towards the comprehensive networking of production plants based on cyber-physical systems. The biggest issue of I 4.0 is still the handling and utilisation of the massive amounts of data generated.
There are various use cases for this data in today’s networked factories. Some of them are the detection, monitoring, optimisation, filtering, and forecasting of production based on the generated data. Recognition is mainly concerned with the identification of text, images or objects involved in the production. Monitoring is primarily used for quality management to ensure the correct process flows. An optimisation is, in turn, intended for the redesign of work steps. This can be done, for example, based on process mining. The optimisation is also the most frequently presented concept in the literature, which is addressed by AI.
Various AI methodologies are used for all these data processing options. Furthermore, the best known and most widely used is that of neural networks. But also metaheuristics or fuzzy systems are becoming more and more critical with varying requirements. Here, depending on the conditions, one or more methods must be selected to exploit the full potential of artificial intelligence.
These developments show that AI already addresses essential passages of production, but in many aspects still receives little attention. This should be reconsidered in the future, as the added value can also be integrated into the other areas presented and can also have a significant influence on the business result there. The applied methodologies should also be better adapted to the respective applications in the future to use existing synergy effects.
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