Towards a Semantic Approach to Detection of Quality Issues in Manufacturing 4.0

Léa Charbonnier, Franco Giustozzi, Julien Saunier, Cecilia Zanni-Merk

Abstract: Quality assurance in manufacturing companies is an essential process for ensuring that products meet established standards. It contributes to customer satisfaction, as well as the reduction of the costs associated with defects. With Quality 4.0, an extension of Industry 4.0 to quality assurance, new possibilities in terms of product quality management are emerging. Thanks to expert knowledge, data collected by machine sensors can be used to anticipate quality issues or manufacturing errors. To semantically detect those situations, an ontology representing manufacturing knowledge linked to quality detection situations is needed. Moreover, as heterogeneous data streams have to be integrated, a combination of stream processing and of-line reasoning can be used. This combination allows a continuous process of data and the use of expert knowledge to detect anomalies. This paper presents an approach for detecting manufacturing quality losses. Therefore, an ontology-based context for manufacturing is introduced to detect quality issues situations. Then, an extension of an existing model using stream reasoning to process heterogeneous data from sensors and predictions is presented to detect the situations continuously.