Object detection characteristics in a learning factory environment using YOLOv8

Artificial intelligence (AI) is of fundamental importance in Industry 4.0. The analysis of sensor data with AI can be utilised for the reliable recognition of complex patterns in real time, which is often a challenging task for humans [1]. For example, in predictive maintenance, AI may in this way help to identify and replace machine parts before they break. More generally, main goals in predictive maintenance are to reduce production downtime and lowering the risk of damages in a factory [2, 3, 4], which may require an exact monitoring of the status of the factory and its processing of workpieces. Other possible applications of AI in Industry 4.0 include robot automatisation, supply chain optimisation and quality control [5, 6]. The latter is significant to maintain a high-level standard and to ensure that there are no harmful components or substances introduced into a production process. Companies are facing the challenge of adopting the concepts of Industry 4.0 in their operations. To foster this development, the use of learning factories may be considered. A learning factory is a model in which learners can develop an understanding of practical problems from the real world, without tinkering with a real factory process [7].