In the early stages of concurrent engineering, the ability to assess design change impact is fundamentally limited by the availability of expert knowledge. Knowledge-Based Engineering (KBE) provides structured approaches for the capture, formalization, management, and diffusion of knowledge within complex organizations. KBE has increasingly turned toward ontology-based methodologies, leveraging their robust framework for shared conceptualization and reasoning capabilities. Integrated with Model-Based Systems Engineering (MBSE), such Ontology-Based Engineering (OBE) methodologies provide the necessary infrastructure for knowledge-driven workflows in a Digital Engineering (DE) context. Such integration is critical for complex engineering sectors such as the aerospace industry. However, the traditional knowledge acquisition process is expert-centric and, consequently, resource-intensive. The digital transformation of the industry has led to an explosion of data volumes, and raised concerns toward statistical approaches. This study implements a hybrid knowledge acquisition method within the OBE framework and MBSE environment. Specifically, this method combines human expertise and interpretable machine learning techniques to formalize knowledge models and instantiate them with concrete design rules. Applied in a real-world use-case involving workload estimation, this paper aims to enhance cross-domain collaboration during the conceptual design phase of new aircrafts.
Duverger et al. (Fri,) studied this question.