Session: 03-05-01: Integrated Computational Materials Engineering

Paper Number: 121060

121060 - Practical Applications of the Material Modeling Approach in the Aerospace Industry 

As the aerospace industry steadily increases the use of Model-Based Engineering, digital twins are rapidly preceding physical counterparts in all types of applications. Virtual testing, which involves the use of digital twins to accurately replicate the results of physical tests, provides a significant competitive advantage.  A powerful analytical methodology that enables virtual testing to failure is referred to in academia and industry as the material modeling approach. In this approach, basic material characterization data from simple and inexpensive coupon-level tests are utilized to develop generalized 3D material constitutive equations using continuum mechanics. These in turn are condensed into code referred to as ‘material subroutines’ that can then be interfaced with commercial Finite Element software for general purpose structural modeling of failure.  In effect, the ‘material subroutine’ essentially forms the digital twin of the material.

The main advantage of this approach is in its capability to model structural failure based on material characterization data from simple easy-to-perform tests.  This enables virtually evaluating structural-level characteristics such as the ultimate strength of an aerospace assembly very early in the design cycle when material selections may not even be finalized.  Various material combinations and other design parameters can be virtually experimented and a variety of trade-off decisions can be made.  Moreover, the independence of the material model from structural considerations enables applications to structures of all forms and sizes.   In other words, once the material subroutine has been developed, tested and finalized, it can be used for predicting the strength at any scale of structural application ranging from a bolt to a wing spar.

The material modeling approach has been widely utilized within the Boeing Company.   The proposed paper will include various practical examples covering different modes of failure.  These include the utilization of a titanium Ti-6Al-4V material model that has been extensively utilized in design, production and fleet-support issues.  Correlations will be demonstrated with test data for dozens of geometrically complex parts such as lug assemblies and fittings, where the predicted ultimate strength matches the test-measured ultimate strength to within 10%.   The application of these methods to resolve a production issue on an airplane program will also be covered to demonstrate the versatility of these methods.   Test data correlations will also be illustrated for other failure modes such as structural buckling of aerospace components.   Moreover, pathfinder applications such as those for predicting fracture strength, ballistic impact and additive manufacturing applications will also be shown along with correlations to equivalent test data.

The coupling of the material modeling approach with robust parametric modeling capability for rapidly creating complex geometries enables virtual Design-Of-Experiments (DOE) to be quickly set up and run.  This in turn enables huge cost savings by reducing the size of the equivalent physical DOEs.  Moreover, the knowledge and understanding gained from a virtual DOE directly feeds into product development.  This coupled with the automation capability in turn enables Machine Learning (ML) applications where the data consumed by the ML algorithms is not limited to a few expensively-obtained test data points, but of a data set that is orders of magnitude larger and composed primarily of virtual test results in addition to those from physical testing. 

In summary, the paper provides a comprehensive overview of the above topics as pertaining to applications at the Boeing Company in a wide range of products ranging from commercial airplanes, geostationary satellites and hypersonic vehicles.

Presenting Author: Mahesh Chengalva Boeing

Presenting Author Biography: Mahesh is an Associate Technical Fellow in the Product Development organization at Boeing Commercial Airplanes (BCA). His Boeing career started at Boeing’s Defense and Space division in Mesa, Arizona, working on the Apache attack helicopter. Later, as part of the team that designed the 787-9 Side-of-Body airframe structure, he transferred to the Seattle area to work at BCA. He led a team developing automated strength prediction methods for metallic, composite and additively manufactured aerospace structures that have enabled virtual testing for a range of applications. Mahesh has ten issued US patents, has a Ph.D. in Mechanical Engineering and has contributed to numerous technical publications and presentations throughout his career.