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Pourjam, Mehrdad

Publications and source records attributed to Pourjam, Mehrdad.

2 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

On the Mechanical Behavior of LP-DED C103 Thin-Wall Structures

Laser Powder Directed Energy Deposition (LP-DED) can produce thin-wall features on the order of 1 mm. These features are essential for large structures operating in extreme environments such as regeneratively cooled nozzles and heat exchangers, which often make use of refractory metals. In this work, the mechanical behavior of LP-DED C103 was investigated via quasi-static tensile testing and low cycle fatigue (LCF) testing. The effects of vacuum stress relief (SR) and hot isostatic pressing (HIP) heat treatments were investigated for specimens in the vertical and horizontal build orientations during tensile testing. The AB and SR properties were lower than literature values for wrought and laser powder bed fusion (L-PBF) bulk components but higher than electron beam powder bed fusion (EB-PBF). The application of a HIP cycle improved strength by 7% and ductility by 27% past the initial as-built condition. Fracture images reveal that interlayer stress concentration sites are responsible for fracture in specimens in the vertical orientation. Meanwhile, fracture in the horizontal specimens mainly propagates at a slanted angle typical of plane stress conditions. The LCF results show cycles to failure ranging from 100 cycles to 8000 cycles for max strain levels of 2% and 0.5%, respectively. Fractography on the fatigue specimens reveals an increasing propagation zone as max strain levels are increased. The impact of these findings and future work are discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗