Sneha Prabha Narra

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

• Fatigue-based process window for laser beam powder bed fusion additive manufacturing

  International Journal of Fatigue · 2024-06-07 · 18 citations

  articleOpen accessSenior authorCorresponding

  Processing defects remain the primary cause for fatigue failure of laser beam powder bed fusion (PBF-LB) produced components. Accordingly, process mapping methodologies have been extensively developed to identify optimal processing parameters to avoid defects. For structure-critical applications, it is necessary to validate the defect-based process maps through fatigue testing. We quantify the defect structure (porosity) process map for PBF-LB Ti-6Al-4V based on defect populations and fatigue pr…

  PublisherDOI

• Impact of melt pool geometry variability on lack-of-fusion porosity and fatigue life in powder bed fusion-laser beam Ti–6Al–4V

  Additive manufacturing · 2024-09-01 · 14 citations

  articleOpen accessSenior author
  PublisherDOI

• Prediction of the powder catchment efficiency and melt track height in laser directed energy deposition

  Journal of Manufacturing Processes · 2025-02-05 · 12 citations

  articleOpen accessSenior authorCorresponding

  Powder catchment and melt track height are foundational for build planning in powder blown laser beam directed energy deposition. However, the interconnected relationships of the catchment efficiency with laser parameters, powder size distribution, and carrier gas flow rate make build planning across machines and feedstock challenging without trial-and-error verification. The primary geometry-based catchment model from laser cladding assumes that this relationship is captured through knowledge o…

  PublisherDOI

• Data-driven inpainting for full-part temperature monitoring in additive manufacturing

  Journal of Manufacturing Systems · 2024-10-19 · 11 citations

  articleOpen access

  Understanding the temperature history over a part during additive manufacturing (AM) is important for optimizing the process and ensuring product quality, as temperature impacts melt pool geometry, defect formation, and microstructure evolution. While in-process temperature monitoring holds promise for evaluating the part quality, existing thermal sensors used in AM provide only partial measurements of the temperature distribution over the part. In this work, we introduce an innovative approach…

  PublisherDOI

• Multi-Lattice Topology Optimization Via Generative Lattice Modeling

  Journal of Mechanical Design · 2024-12-24 · 6 citations

  article

  Abstract Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space int…

  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)

Similar researchers at Carnegie Mellon University

• Alan McGaugheyh-53
• Philip LeDuch-50