Material Data Ready for Artificial Intelligence

Those who develop sustainable and smart products can take advantage of artificial intelligence. However, AI requires high-quality data: Only when data from the lab, simulations, and production align can it learn from it and enable better materials, products, and processes. Often, however, this data speaks different “languages.” This is where Janik Harter, a doctoral candidate at the Institute for Digital Engineering and Production (IDEeP) at HSO, comes in. Using the example of new, more environmentally friendly chromium coatings, he is developing a kind of shared dictionary—a so-called ontology. It makes material data understandable to both humans and computers, and thus usable by AI. For this work, he received the Best Poster Award at the general assembly of the MaterialDigital platform at TU Darmstadt. In addition, Janik Harter, Prof. Dr. Thomas Seifert, and student Thitichai Janpheng are co-authors of a research article on the AI-readiness of material data, published by 68 researchers from academia and industry.

Contact:
Prof. Dr. Thomas Seifert,
Hochschule Offenburg,
thomas.seifert@hs-offenburg.de