EIS Lab homeResearch Snapshots, Accolades, ...Faculty, Staff, Students, and AlumniPapers, Reports, Theses, ...Analysis Theory & Methodology, X-Analysis Integration (XAI), Change Management, Engineering Information Technology, ... Projects, Sponsors, Toolkits, ...Conferences, Workshops, Thesis Presentations, ... Georgia Tech search engineCourses, Tools, Related Organizations, Directions & Locale Guides, ...

Characterizing Fine-Grained Associativity Gaps: A Preliminary Study of CAD-CAE Model Interoperability


Peak R. S. (Sept 2-6, 2003) Characterizing Fine-Grained Associativity Gaps: A Preliminary Study of CAD-CAE Model Interoperability. Paper DETC2003/CIE-48232, ASME Design Engineering Technical Conferences, Chicago.


fine-grained associativity gap, design-analysis integration, CAD-CAE interoperability, knowledge-based engineering (KBE) patterns, multi-representation architecture (MRA), constrained object (COB)


This paper describes an initial study towards characterizing model associativity gaps and other engineering interoperability problems. Drawing on over a decade of X-analysis integration (XAI) [1] research and development, it uses the XAI multi-representation architecture (MRA) as a means to decompose the problem and guide identification of potential key metrics.

A few such metrics are highlighted from the aerospace industry. These include number of structural analysis users, number of analysis templates, and identification of computing environment components (e.g., number of CAD and CAE tools used in an example aerospace electronics design environment).

One problem, denoted the fine-grained associativity gap, is highlighted in particular. Today such a gap in the CAD-CAE arena typically requires manual effort to connect an attribute in a design model (CAD) with attributes in one of its analysis models (CAE). This paper estimates that 1 million such gaps exist in the structural analysis of a complex product like an airframe. The labor cost alone to manually maintain such gaps likely runs in the tens of millions of dollars. Other associativity gap costs have yet to be estimated, including over- and under-design, lack of knowledge capture, and inconsistencies.

Narrowing in on fundamental gaps like fine-grained associativity helps both to characterize the cost of today's problems and to identify basic solution needs. Other studies are recommended to explore such facets further.

[1] X = design, mfg., sustainment, and other lifecycle phases.


Manuscript: pdf

Presentation: ppt

Earlier Related Work