Another observation from Figure examples #4 and #6 is that materials are detected successfully by both model variants. In example #6, the material names are contained in a keyword list which indicates that contextual information is not mandatory to identify materials but that some materials and standards such as SAE or EN as well as trade names are memorized or local correlations are learned (concatenated chemical elements → material). Unlike the unified nomenclatures in chemistry, materials science lacks universal notations , leading to diverse representations for the same material. For instance, the trade name 'Inconel 725', associated with a nickel-chromium-molybdenum-niobium alloy, might be readily recognized, however, linking with materials numbers (2.4857) and a breakdown of chemical compositions (Ni(balance), Cr(19-22.5%), Mo(7-9.5%), Nb(2.75-4.0%)) can present significant hurdles. This difficulty stems from the inherent ambiguity and lack of context within these notations. The abbreviated forms often only contain information about major alloying elements, neglecting crucial information like the presence of other micro-alloying constituents. In this work, a rather small quantity of publications -and by extension alloy classes -was considered. Thus, the generalization to arbitrary alloy systems and non-metallic materials needs to be explored in the future. Assuming the model can reliably identify materials through characteristic patterns within materials nomenclature standards, an approach could be envisioned where the materials definitions are automatically validated against an additional database as well as harmonized and enriched.