@inproceedings{a2aec00450b04deba3206842956676f7,
title = "A forecasting model-based discovery of causal links of key influencing performance quality indicators for sinter production improvement",
abstract = "Sintering is a complex production process where the process stability and product quality depend on various parameters. Building a forecasting model improves this process. Artificial intelligence (AI) approaches show promising results in comparison to current physical models. They are mostly considered black box models because of their hidden layers. Due to their complexity and limited traceability, it is difficult to draw conclusions for real sinter processes and improving the physical models in a running plant. This challenge is addressed by focusing on detecting causal links from AI-based forecasting models in order to improve the understanding of sintering and optimizing existing physical models.",
keywords = "Causality detection, Machine learning, Quality control, Sintering, Visual analytics",
author = "Matej Vukovic and Vaishali Dhanoa and Markus J{\"a}ger and Conny Walchshofer and Josef K{\"u}ng and Petra Krahwinkler and Belgin Mutlu and Stefan Thalmann",
year = "2020",
doi = "10.33313/380/218",
language = "English",
series = "AISTech - Iron and Steel Technology Conference Proceedings",
publisher = "Iron and Steel Society",
pages = "2028--2038",
booktitle = "Proceedings of the Iron and Steel Technology Conference, AISTech 2020",
address = "United States",
note = "AISTech 2020 Iron and Steel Technology Conference ; Conference date: 31-08-2020 Through 03-09-2020",
}