Dr. M. G. Glavicic
,
Rolls-Royce Corporation, Indianapolis, IN
Dr. Vasisht Venkatesh
,
Pratt & Whitney, East Hartford, CT
Dr. Iuliana Cernatescu
,
Pratt & Whitney, East Hartford, CT
Dr. Thomas F. Broderick
,
GE Aviation, Cincinnati, OH
Mr. Vikas Saraf
,
ATI Ladish, Cudahy, WI
Dr. Ian Demptster
,
Wyman-Gordon, Houston, TX
Dr. Kayla Calvert
,
Timet, Henderson, NV
Dr. Sesh Tamirisakandala
,
Arconic Titanium & Engineered Products, Niles, OH
Dr. Ayman salem
,
Materials Resources LLC, Dayton, OH
Mr. Ravi Shankar
,
Scientific Forming Technologies Corporation, Columbus, OH
Dr. William Mulsinski
,
Air Force Research Laboratory, WPAFB, OH
Dr. Paul Shade
,
Air Force Research Laboratory, WPAFB, OH
The progress of two current MAI programs: 1) Advanced Titanium Alloy Fatigue Modeling (RR-12) and 2) Incorporation of Near Surface Residual Stresses into the Advanced Titanium Alloy Fatigue Modeling Program (RR-13) will be discussed. The goal of these programs is to create a cross-functional team focused on developing the necessary integrated computational materials engineering (ICME) framework, knowledge, and supporting database to model and predict location-specific fatigue properties across the entire titanium supply chain, and validate this ICME framework on complex production components. The projects address the extensive variation that can be produced in titanium microstructures, and the effect this has on critical fatigue properties. In addition to microstructure effects, the effects of various shot peen processes used in most components is addressed.
This work is built upon the foundation established in prior titanium modeling projects such as the Advanced Titanium Alloy Microstructure and Mechanical Property Modeling program (RR-10). The current work involves the development of a complex Bayesian neural network fatigue life prediction model that takes into account the evolution of local microstructure and crystallographic texture during thermomechanical processing. In addition, the effects of surface residual stresses imposed by peening processes were examined and models were developed for integration into the commercially available deformation processing simulation tool DEFORM. This approach ensures the transition of the program results to industry, for broad application.
Implementation of this technology will include reduced manufacturing costs and cycle-time for existing components via process optimization, reduced product development time and cost for new components through reduced shop floor trials, and component life-cycle cost savings through performance improvements.
