AI can be used in the ceramics industry to optimize production processes and achieve higher efficiency. For example, the furnace temperature can be automatically monitored and controlled to achieve optimal strength and density of the products, or to minimize energy consumption while maintaining product quality.
At the HTL, AI algorithms are used in the creation of digital twins of furnaces. A digital twin is a virtual version of a real furnace based on data from sensors and other sources. With a digital twin, various scenarios can be simulated and optimized to improve oven performance and minimize downtime.
AI algorithms are used in the creation of the digital twin of the furnace, such as for automated data analysis of measurement data from a real furnace, based on which the digital twin is created and validated. However, the creation of a digital twin based on a finite element (FE) or computational fluid dynamics (CFD) model of the furnace usually involves a large amount of computation, which limits real-time capability.
An extension is the AI-based digital twin of the furnace, which makes it possible to monitor thermal processes in real-time. This monitoring can improve reaction time to unforeseen events. To achieve this, an AI model is trained on the simulation results of the classical digital twin of the furnace and then used for prediction.
Alternatively, AI models can also be trained directly on historical data from the furnace to make predictions about future performance. By analyzing correlations in the furnace's performance data, patterns can be identified, and potential failures can be detected early in the production process.