Closing gaps in the digital thread with the Quality Information Framework (QIF) standard for a seamless geometry assurance process
An essential premise for a reliable variation simulation is that information on the geometrical part variations and their accumulation and propagation within an assembly is available, accessible, interchangeable, and usable in all geometry-related downstream activities. For this reason, this article studies the potential of the QIF (Quality Information Framework) standard. It illustrates how it can be used in the sense of Model-Based Definition to close gaps in the digital geometry assurance process. Besides benefits in the automation of variation simulation, it demonstrates that the semantic, feature-based linkage between product specification and inspection information in QIF 3.0 facilitates the augmentation of variation simulation with more detailed feature information for pre-production applications and feeding the digital twin to assure and optimize product quality in the production phase. • Enhancing variation simulation with the Quality Information Framework (QIF) standard. • Mapping “as-design” and “as-inspected” QIF 3.0 information to surface meshes for variation simulation. • Mesh segmentation with Boundary Representation information from QIF 3.0. • Using QIF 3.0 for statistical predictions and digital twin applications in geometry assurance.
Authors
- Martin Roth (ORCID: https://orcid.org/0000-0001-8166-202X)
- Abolfazl Rezaei Aderiani (ORCID: https://orcid.org/0000-0002-2720-9838)
- Rikard Söderberg (ORCID: https://orcid.org/0000-0002-9138-4075)
- Kristina Wärmefjord (ORCID: https://orcid.org/0000-0003-1556-3319)
- Edward Morse (ORCID: https://orcid.org/0000-0003-1227-700X)
Institutions
- University of North Carolina at Charlotte (US)
- Chalmers University of Technology (SE)
Publication Details
- Journal
- Computer-Aided Design
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.cad.2025.103860
- Citations
- 4
- Primary Topic
- Manufacturing Process and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 4.97
Funders
- VINNOVA