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Saikumar R Yeratapally

Publications and source records attributed to Saikumar R Yeratapally.

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

A Computational Study to Investigate the Effect of Defect Geometries on the Fatigue Crack Driving Forces in Powder-Bed AM Materials

Powder-bed additive manufacturing (AM) processes are associated with the formation of multiple types of process-specific pores, including but not limited to lack-of-fusion (LoF) and keyhole pores. The performance of an AM component is dependent on the type of pores, their density and their proximity to the free surface, and other heterogeneities in the microstructure. In order to characterize the influence of porosity on the mechanical behavior of AM materials, it is imperative to quantitatively analyze the heterogeneous strain accumulation in the vicinity of porosity. Process-specific microstructure models are generated using SPPARKS, an open-source process simulation code. Spherical keyhole or irregular LoF pores are embedded into the microstructure models, which are meshed and input into a finite element code, ScIFEN, to solve for the heterogeneous strain localization in the vicinity of the pores. Given the non-smooth geometries of LoF pores, they readily promote strain accumulation in their vicinity thereby increasing the propensity of initiating fatigue cracks.

Saikumar R Yeratapally↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32709-DND

Outline - Process-Structure-Performance Framework - Process-Specific Defects in selective laser melting (SLM) additive manufacturing (AM) process - Idealized Keyhole/Entrapped gas pores - Idealized Lack of Fusion (LoF) pores - Influence of Pore Geometry on Strain Localization - 3D Model of LoF pores - Partial validation of the 3D LoF 2 model

Saikumar R Yeratapally↗
NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗
NASA NTRSDate not supplied

Uncertainty Reduction With Multi-Model Monte Carlo for Crystal Plasticity Simulations of Additively Manufactured Metals

In this work, multi-model Monte Carlo estimators are developed to reduce uncertainty in quantities of interest (QoIs) extracted from crystal plasticity simulations of additively manufactured (AM) metals. A significant concern in AM parts is uncertainty in mechanical properties caused in part by complex microstructures that arise from the AM process. Quantifying uncertainty in microstructure-sensitive behavior using experiments alone is costly, especially when mechanical allowables must be established. Quantitative relationships among microstructure, micromechanical metrics like slip accumulation, crack initiation, and failure are also difficult to capture with limited experiments. Crystal plasticity material models instead enable computational prediction of micromechanical stress and strain fields given a discretized microstructure. However, high-fidelity finely discretized crystal plasticity simulations are computationally expensive, while lower-fidelity models are less accurate and generally biased, making uncertainty quantification and reduction computationally difficult as well. Multi-model Monte Carlo methods leverage correlations between high- and low-fidelity models to produce unbiased estimators for QoIs with reduced uncertainty relative to standard Monte Carlo. Crystal plasticity QoIs considered in this work include yield strength and the mean and extreme values of micromechanical fields that are relevant to crack initiation. Multi-model Monte Carlo estimators are developed for each individual QoI and several groups of QoIs. The results of this work establish relationships among model correlations, sample allocation, and uncertainty reduction for different combinations of QoIs and demonstrate a trend of less uncertainty reduction as QoIs become more sensitive to local microstructure. Limitations from using pilot samples to estimate model covariances and train low-fidelity models are also addressed. The uncertainty reduction achieved by multi-model Monte Carlo is an important step toward using computational mechanics models to predict microstructure-sensitive crack initiation and failure in AM parts.

uncertainty quantification↗