The project focuses on exploring the potential of AI solutions for the needs of the manufacturing industry. In particular, the project will focus on AI in industrial process quality monitoring and the use of big data generated in battery cell testing as part of the battery chemical manufacturing process.
A lot of data is produced and collected from industrial processes. Industrial processes are monitored by technical sensors that generate huge amounts of data on the state of the process. This data is supplemented by laboratory measurements. The data thus produced forms the basis on which the performance of industrial processes is typically assessed. However, the cost and challenge of directly measuring and analysing quality variables has increased. However, in order to monitor the status of systems, to implement smooth process control and to improve product quality, it would be important to be able to monitor key quality indicators quickly and accurately. The project will explore the potential of different AI techniques to classify large data sets, detect anomalies and develop virtual sensors. A virtual sensor is software that combines and processes data from different sources to generate new information.
Another challenge for battery research is to make use of the large amount of data. In battery materials research, it is important to know the performance of the material in the battery cell, i.e. in the final application. The testing of the cell generates a lot of data that is currently not exploited to predict the electrochemical behaviour of different types of battery chemicals and to build models. This allows the introduction of new data/knowledge and reduces the need for manual data processing.
The objectives of the project can be summarised as follows
Information technology, teaching, research and projects
Head of Information Technology Unit
Information technology, research and projects
Information technology, research and projects
Kokkola University Consortium Chydenius
Information Technology