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About this Abstract
Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Integrated Generative and Predictive Machine Learning Models for Estimation of Microstructure and Cold Dwell Fatigue Life in Ti-6Al-4V Forgings
Author(s) Ryan Noraas
On-Site Speaker (Planned) Ryan Noraas
Abstract Scope The cold dwell fatigue (CDF) mechanism in titanium alloys has demonstrated component and system performance impact in isolated cases for nearly 4 decades. Significant efforts by researchers across industry, government, and academia have advanced the understanding of CDF through computational modeling, experimental testing and material characterization of alloys such as Alloy-834, Ti-6242 and Ti-64. Accurately predicting and addressing cold dwell fatigue (CDF) risk in critical aerospace components has driven conservative methods to ensure safety. This work will highlight results from a recently completed Metals Affordability Initiative (MAI) project that linked forging and heat treat process models with conditional generative models to predict location-specific microstructure and microtexture for Ti-64. Convolutional neural network (CNN) models were used to predict mean and variation in location-specific mechanical properties (tensile yield strength and CDF life) from generated microstructure images. Key results and outcomes of this project will be presented.
Proceedings Inclusion? Undecided

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