A Collaborative, Role-Based Knowledge Framework for Structural Functional Layers (SFL)
Abstract
This paper introduces a collaborative, role-based knowledge framework for Structural Functional Layers (SFL) that organizes materials, interfaces, and process knowledge independently of concrete system implementations. Instead of treating materials as system-defining elements, the framework structures shared knowledge according to functional roles within multilayer composite systems. Materials are formalized as interchangeable role carriers, while functional intent, material choice, and interfacial behavior are explicitly decoupled. This separation enables functional conflicts—such as those between electrical conductivity, optical transparency, mechanical compliance, and environmental protection—to be identified and resolved at the architectural level prior to material selection or optimization. In contrast to conventional material databases or application-specific case collections, the presented approach focuses on the structure and governance of shared knowledge itself. It defines a role-based indexing scheme, compatibility relations, and consistency rules for collaborative extension. The resulting knowledge substrate complements existing SFL-based architectural frameworks and provides a stable foundation for downstream simulation, optimization, and AI-assisted workflows without embedding such tools directly.
Fields of Study & Subjects
Material Selection and Properties
Systems Engineering Methodologies and Applications
Machine Learning in Materials Science
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 6 February 2026
- Language
- EN
- Document type
- Preprint