About

Sneha Prabha Narra is an Assistant Professor in the Mechanical Engineering Department at Carnegie Mellon University, with a courtesy appointment in Materials Science and Engineering. She received her B.E. in civil engineering from Osmania University in 2012, followed by multiple advanced degrees from Carnegie Mellon University, including an M.S. in computational mechanics in 2013, an M.S. in mechanical engineering in 2015, and a Ph.D. in mechanical engineering in 2017. Her postdoctoral training was conducted at the NextManufacturing Center at Carnegie Mellon University. Her research interests focus on additive manufacturing, advanced manufacturing, digital twins, machine learning, manufacturing workforce, materials characterization, and robotics. She has contributed to the field through her involvement in organizing additive manufacturing symposia and workshops, serving as the Associate Editor of the Additive Manufacturing journal, and participating in various research initiatives such as predicting failure in manufacturing processes and developing digital backbones for manufacturing data. Prior to her current role, she served as an assistant professor at Worcester Polytechnic Institute for three years. Narra has been recognized with awards including the NSF CAREER award and has been actively involved in advancing manufacturing research and education.

Research topics

• Composite material
• Materials science
• Metallurgy
• Computer Science
• Nanotechnology
• Optics

Selected publications

• Surrogate model for rapid laser powder bed fusion distortion prediction with adjustable material property input

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Dataset of Processed X-ray Computed Tomography Scans Comparing Segmentation Methods from AMBench 2025-03 Rotating Bending Fatigue Challenge

  KiltHub Repository · 2026-03-11

  datasetOpen access

  This dataset contains our segmentation results and models for segmenting X-ray Computed Tomography Scans of Laser-Powder Bed Fusion Additively Manufactured Rotating Bending Fatigue Specimens as part of the NIST AMBench 2025-03 Challenge. The initial dataset containing the raw scans and the segmentation method that was provided is available at https://doi.org/10.18434/mds2-3734.

  PublisherDOI

• Property optimization through full-part thermal history control in laser powder bed fusion additive manufacturing

  Additive manufacturing · 2026-04-23

  articleOpen accessSenior authorCorresponding

  Metal additive manufacturing (AM) enables rapid, on-demand production of parts ranging from prototypes to mission-critical components. However, achieving high-strength metallic components often relies on post-processing heat treatments for many alloys, adding time and cost. For instance, in Alloy 718, a common high-strength alloy used in laser powder bed fusion (L-PBF) AM, strengthening primarily relies on precipitation hardening during an aging heat treatment. Importantly, heating during the deposition process can also elevate temperatures into the precipitation hardening range, causing in-situ aging. Thus, this work leverages the elevated temperatures during fabrication to enable controlled in-situ aging that increases as-fabricated hardness and improves uniformity. For the first time, this is realized through full-part thermal history control during L-PBF fabrication of Alloy 718. The method embeds an experimentally fitted material hardening model in an axisymmetric lumped-layer thermal simulation to predict in-situ part hardness. The resulting thermal and hardness dynamics model is then used in conjunction with a trajectory optimization algorithm to determine time-varying laser power and baseplate temperature profiles. These optimized process conditions target a uniform hardness of 450 HV in an inverted cone geometry by intentionally inducing in-situ precipitation hardening. The planned trajectory increased the mean hardness and improved uniformity from 374 ± 41 HV to 439 ± 29 HV without a separate post-process aging heat treatment. These results are repeatable within 8% and establish a path to integrate microstructural aging control directly into the deposition step to achieve high-strength metallic components without an additional post-process heat treatment step. • Demonstrated high-throughput time–temperature-hardness characterization methodology. • Applied a modified Avrami equation to model continuous cooling transformations. • Predicted part hardness during printing using thermal history simulations. • Determined optimal power and baseplate temperature to maximize hardness.

  PublisherDOI

• Dataset of Processed X-ray Computed Tomography Scans Comparing Segmentation Methods from AMBench 2025-03 Rotating Bending Fatigue Challenge

  KiltHub Repository · 2026-03-11

  datasetOpen access

  This dataset contains our segmentation results and models for segmenting X-ray Computed Tomography Scans of Laser-Powder Bed Fusion Additively Manufactured Rotating Bending Fatigue Specimens as part of the NIST AMBench 2025-03 Challenge. The initial dataset containing the raw scans and the segmentation method that was provided is available at https://doi.org/10.18434/mds2-3734.

  PublisherDOI

• Influence of Melt Pool Overlap on Inclusion Entrapment and Dispersoid Characteristics in Oxide Dispersion-Strengthened Ni-20Cr Fabricated by Powder Bed Fusion - Laser Beam

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Extreme value statistics with uncertainty to assess porosity equivalence across additively manufactured parts

  Reliability Engineering & System Safety · 2025-05-09 · 4 citations

  articleOpen accessSenior authorCorresponding

  Fatigue performance in Powder Bed Fusion – Laser Beam is influenced by the largest pore size within the stressed volume, which correlates with fatigue life in porosity-driven failures. However, single value estimates for the largest pore size are insufficient to capture the experimentally observed scatter in fatigue properties. To address this gap, in this work, we incorporate uncertainty quantification into extreme value statistics to estimate the largest pore size distribution in a given volume of material by capturing uncertainty in the number of pores present and the distribution parameter estimates. We then applied this statistical framework to compare the porosity equivalence between two geometries: a 4-point bend fatigue specimen and an axial fatigue specimen in the gauge section. Both geometries were manufactured with the same process conditions using Ti-6Al-4V, followed by porosity characterization via X-ray Micro CT. The results show that the largest pore size distribution of the 4-point bend specimen is insufficient to accurately capture the largest pore size observed in the axial fatigue specimen, despite similar dimensions. Our findings highlight the need for rigorous statistical analysis to quantify the differences between porosity distributions. • Incorporated uncertainty in Generalized Pareto Distribution parameters. • Applied to X-ray Micro CT data of additively manufactured fatigue specimens. • Estimated the largest pore size distribution with model uncertainty. • Largest pore size distribution varies with specimen geometry.

  PublisherDOI

• Trajectory Optimization for Spatial Microstructure Control in Electron Beam Metal Additive Manufacturing

  2025-07-08

  articleSenior author

  Metal additive manufacturing (AM) opens the possibility for spatial control of as-fabricated microstructure and properties. However, since the solid state diffusional transformations that drive microstructure outcomes are governed by nonlinear ODEs in terms of temperature, which is itself governed by PDEs over the entire part domain, solving for the system inputs needed to achieve desired microstructure distributions has proven difficult. In this work, we present a trajectory optimization approach for spatial control of microstructure in metal AM, which we demonstrate by controlling the hardness of a low-alloy steel in electron beam powder bed fusion (EB-PBF). To this end, we present models for thermal and microstructural dynamics. Next, we use experimental data to identify the parameters of the microstructure transformation dynamics. We then pose spatial microstructure control as a finite-horizon optimal control problem. The optimal power field trajectory is computed using an augmented Lagrangian differential dynamic programming (AL-DDP) method with GPU acceleration. The resulting time-varying power fields are then realized on an EB-PBF machine through an approximation scheme. Measurements of the resultant hardness shows that the optimized power field trajectory is able to closely produce the desired hardness distribution.

  PublisherDOI

• Dataset of process parameters, melt track geometry, powder catchment, and particle stream measurements for the laser beam directed energy deposition of AISI 316L

  Data in Brief · 2025-07-25

  articleOpen accessSenior authorCorresponding

  This dataset reports the characterization and data processing methodology of 45 individual AISI 316L single melt tracks, fabricated by powder blown laser beam directed energy deposition (DED-LB) metal additive manufacturing. The melt tracks were deposited across a parametric combination of process parameters: powder size distributions, carrier gas flow rates, and laser spot diameter-laser power sets. The measured melt track properties include the average melt track width, height, cross-sectional area, and the powder catchment efficiency. Optical profilometry was used to extract the melt track dimensions and to calculate the powder catchment efficiency. In addition, the corresponding particle stream spatial distributions and particle velocity distributions were measured across the deposition flow parameters by processing high-speed image data. The median particle Stokes number for each flow condition was reported for comparability with other discrete coaxial nozzle systems with particle-laden flows. This dataset can aid in the validation of computational simulations of particle-laden flows from three-jet nozzle systems and the validation of DED-LB models which predict the melt track properties from known process parameters.

  PublisherDOI

• Evolution of powder-entrapped pores in Ti–6Al–4V fabricated with powder bed fusion-laser beam process

  Additive manufacturing · 2025-06-18 · 3 citations

  articleOpen accessSenior authorCorresponding

  X-ray micro computed tomography (X- μ CT) of bulk powder bed fusion - laser beam (PBF-LB) Ti-6Al-4V samples shows that, within the optimal process window – where lack-of-fusion and keyhole porosity are minimized – higher laser power reduces the number density of powder-entrapped pores when hatch spacing, layer thickness, and laser spot size remain fixed. To gain insight into this observation, the X- μ CT measurements of powder-entrapped pores are combined with a computational model to simulate pore trajectories in the PBF-LB melt pool. More than 100,000 independent pore trajectories are simulated at two different combinations of laser power and scanning velocity, where the forces acting on the pores are quantified using melt pool temperatures, pressures, and fluid flow velocities from multi-physics simulations. The model is then used to predict the pore size distributions in bulk samples fabricated within the optimal process window at 150 W, 700 mm/s and 370 W, 1200 mm/s. At both laser power settings, the total number density of pores predicted by the model is within one order of magnitude of the experimental values. The model suggests that the differences in the pore size distributions measured with X- μ CT are caused by differences in melt pool overlap (i.e., remelting). Using the model, a process map is constructed to predict porosity as a function of hatch spacing and layer thickness, suggesting that the number density of powder-entrapped pores can vary by two orders of magnitude within the optimal process window. This result suggests that the elimination of powder-entrapped pores poses an obstacle to increasing build rates by increasing the hatch spacing and layer thickness. While previous investigations of pore evolution during PBF-LB focused on experimental approaches, this work will enable the development of model-driven processing strategies to promote pore elimination.

  PublisherDOI

• Two-Color Thermography of GMAW to Enable Real-Time Hardness Prediction

  Welding Journal · 2025-08-01 · 2 citations

  articleOpen accessSenior author

  Advanced process monitoring and model validation are essential for improving weld quality in both welding and welding-based additive manufacturing processes. Specifically, temperature is a key quantity of interest for understanding defect formation and microstructural evolution, which significantly impact mechanical properties. However, achieving accurate in-situ temperature imaging is challenging due to emissivity variations across the dynamic melt pool. To address this, we implemented a two-color imaging technique using a single commercial color camera to reduce temperature readings’ sensitivity to emissivity variations. High dynamic range images during melting were captured at various exposure times, and spatial and temporal filters were applied to minimize interference from the plasma arc emissions. The resulting temperature fields within the melt pool were then utilized to estimate cooling rates, which were further correlated to ex-situ hardness measurements. The strong correlation observed between cooling rates ranging from 20 to 600 K/s and hardness ranging between 250 to 400 HV demonstrated the potential of our easy-to-use two-color thermal imaging setup for preliminary evaluation of mechanical properties in a non-destructive manner. Beyond its significance for predicting mechanical properties, this technique provides a validated temperature measurement approach that can enhance the accuracy of physics-based models, such as those used to predict defect formation mechanisms, like porosity.

  PublisherDOI

Frequent coauthors

• Daniel Gingerich

  University of Virginia

  25 shared

• Stephanie Laughton

  Citadel

  25 shared

• Casey Canfield

  25 shared

• Jack Beuth

  19 shared

• Anthony D. Rollett

  Carnegie Mellon University

  13 shared

• Jiangce Chen

  8 shared

• Christopher McComb

  8 shared

• William Frieden Templeton

  Carnegie Mellon University

  8 shared

Labs

• EMIT LabPI

Education

• Other, Civil Engineering

  Osmania University

  2012

• M.S., Computational Mechanics

  Carnegie Mellon University

  2013

• M.S., Mechanical Engineering

  Carnegie Mellon University

  2015

• Ph.D., Mechanical Engineering

  Carnegie Mellon University

  2017

Awards & honors

• NSF CAREER Award (2021)