Total Inconsistency Management and Optimization in Model-Based Engineering of Industrial Automation Systems

Inconsistencies during the engineering process are common and critical. This work addresses the inconsistencies arising during the cross-disciplinary engineering of automated production systems, where models are used for mechanics, electrics/electronics, and control software. A workflow-oriented framework is developed to identify and handle semantic inconsistencies semi-automatically, by coupling models with domain ontologies. Furthermore, multi-fidelity optimization is proposed to efficiently optimize behavior problems. The feasibility and benefits of the framework are demonstrated through use cases from different automation industries.