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Results for “titanium aluminide”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Design and Development of Low Weight, Titanium Aluminide Airfoils for High Performance Industrial Gas Turbines meeting 65% Combined Cycle Efficiency (Final Report)

This project funded under DOE-FOA-0001816 was performed with the intent of evaluating the potential of utilizing a low density, high specific strength Titanium Aluminide (TiAl) alloy in a large annulus last stage gas turbine blade with the intent of improving combined cycle gas turbine efficiency toward a goal of reaching 65% efficiency. The project consisted of identifying a potentially suitable TiAl material for manufacture of such a large blade by investment casting. This included estimation of design properties for the selected alloy, trade studies using a baseline design created for a nickel based alloy cast in the TiAl alloy to evaluate potential design changes required for the use of this material and optimization of a prototype design for the same baseline application using the selected TiAl alloy. Several rounds of optimization were completed with the intent of meeting the baseline design targets with a resulting design that showed at least equivalent performance in terms of creep, LCF/TMF, HCF and aerodynamic damping potential. While it was found that significant reductions in blade loading on the disk were possible, the design resulting from the optimization met most lifing targets at the baseline size, but showed a reduction in margins in some areas in comparison to the Ni-based baseline design. Additionally, it was found that the low strain tolerance of the TiAl alloy for temperatures at and below the operational temperature of the attachment resulted in marginal, although still acceptable, creep rupture performance of the design. Due to results indicating the reduced potential for increasing turbine annulus compared to expectations a Phase II award was not proposed and it is not recommended to pursue additional design optimization efforts until further material advances with TiAl materials are achieved.

03 NATURAL GAS↗
DOE OSTI2021

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

42 ENGINEERING↗
DOE OSTI2022

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes.

36 MATERIALS SCIENCE↗