Enhancing robotic steel prefabrication with semantic digital twins driven by established industry standards

To increase automation in steel construction, new approaches are needed to strengthen the robustness of robotic steel prefabrication processes against manufacturing tolerances. While Digital Twins (DTs) can enable the detection of deviations and the adaptation of machine control accordingly, an adaptive information model interface that can integrate cross-process and cross-machine considerations is missing. Therefore, this paper focuses on the development of an ontology based on an existing steel prefabrication standard to link process data, tolerances and deviations, thereby enabling the realisation of a semantic DT. The approach’s feasibility is proven by a case study that demonstrates process and tolerance modelling, as well as linking robot control, feedback data, and measured tolerances for robotic plasma cutting. The results show that by means of the resulting data model, a semantic DT can be realised, which allows making deviations and process knowledge available for downstream manufacturing, assembly and construction processes. •Develops a semantic Digital Twin that links process data, deviations and tolerances.•Extends production standards like DSTV-NC and IFC for prefabrication processes.•Demonstrates the proposed DSTV ontology in a case study for robotic plasma cutting.•Enables downstream optimisation using robot control, feedback, and deviations.•Enables deduction of process knowledge from stored Linked Data.