About

Amrita Basak is an Early Career Professor in the Department of Mechanical Engineering at Penn State University. Her research focuses on manufacturing, mechanical sciences, and multiscale and multiphysics modeling, as well as computational analysis. She is affiliated with the Reber Building and can be contacted via aub1526@psu.edu. Her work contributes to advancing solutions in energy needs, homeland security, biomedical devices, and transportation systems, aligning with the department's broader mission of innovation and impact in mechanical engineering.

Research topics

• Computer Science
• Materials science
• Mechanical engineering
• Engineering
• Artificial Intelligence
• Mathematics
• Composite material
• Mathematical optimization
• Physics
• Metallurgy
• Geometry
• Process engineering

Selected publications

• Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

  arXiv (Cornell University) · 2026-04-09

  articleOpen accessSenior author

  Accurate phase equilibria are foundational to alloy design because they encode the underlying thermodynamics governing stability, transformations, and processing windows. However, while the CALculation of Phase Diagrams (CALPHAD) provides a rigorous thermodynamic framework, exploring multicomponent composition-temperature space remains computationally expensive and is typically limited to sparse section. To enable rapid phase mapping and alloy screening, we propose a physics-informed graph attention network (GAT) that learns element-aware representations and couples them with thermodynamic constraints for multi-label phase-set prediction in the Ag-Bi-Cu-Sn alloy system. Using about 25,000 equilibrium states generated with pycalphad, each composition-temperature point is represented as a four-node element graph with atomic fractions and elemental descriptors as node features. The model combines graph attention, global pooling, and a multilayer perceptron to predict nine relevant phases. To improve physical consistency, we incorporate thermodynamic constraints, applied as training penalties or as an inference-time projection. Across six binary and three ternary subsystems, the baseline model achieves a macro-F1 score of 0.951 and 93.98% exact-set match, while physics-informed decoding improves robustness and raises exact-set accuracy to about 96% on dense in-domain grids. The surrogate also generalizes to an unseen ternary section with 99.32% exact-set accuracy and to a quaternary section at 700 °C with 91.78% accuracy. These results demonstrate that attention-based graph learning coupled with thermodynamic constraint enforcement provides an effective and physically consistent surrogate for high-resolution phase mapping and extrapolative alloy screening.

  PublisherOA PDF

• Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

  arXiv (Cornell University) · 2026-04-09

  preprintOpen accessSenior author

  Accurate phase equilibria are foundational to alloy design because they encode the underlying thermodynamics governing stability, transformations, and processing windows. However, while the CALculation of Phase Diagrams (CALPHAD) provides a rigorous thermodynamic framework, exploring multicomponent composition-temperature space remains computationally expensive and is typically limited to sparse section. To enable rapid phase mapping and alloy screening, we propose a physics-informed graph attention network (GAT) that learns element-aware representations and couples them with thermodynamic constraints for multi-label phase-set prediction in the Ag-Bi-Cu-Sn alloy system. Using about 25,000 equilibrium states generated with pycalphad, each composition-temperature point is represented as a four-node element graph with atomic fractions and elemental descriptors as node features. The model combines graph attention, global pooling, and a multilayer perceptron to predict nine relevant phases. To improve physical consistency, we incorporate thermodynamic constraints, applied as training penalties or as an inference-time projection. Across six binary and three ternary subsystems, the baseline model achieves a macro-F1 score of 0.951 and 93.98% exact-set match, while physics-informed decoding improves robustness and raises exact-set accuracy to about 96% on dense in-domain grids. The surrogate also generalizes to an unseen ternary section with 99.32% exact-set accuracy and to a quaternary section at 700 °C with 91.78% accuracy. These results demonstrate that attention-based graph learning coupled with thermodynamic constraint enforcement provides an effective and physically consistent surrogate for high-resolution phase mapping and extrapolative alloy screening.

  PublisherDOI

• Microstructure evolution of IN718 wire with SS316L powder co-deposited using laser directed energy deposition

  Journal of Engineering Research · 2026-05-01

  articleOpen accessSenior authorCorresponding

  This study investigates the evolution of bead geometry, microstructure, and elemental segregation during the co-deposition of Inconel 718 (IN718) wire and 316 L stainless steel (SS316L) powder using coaxial wire-powder laser-directed energy deposition. A systematic experimental approach is employed, progressing from single-layer single-track to single-layer multi-track, and subsequently multi-layer multi-track configurations to evaluate process scalability and stability. The influence of laser power, scan speed, powder feed rate, hatch spacing, and layer height on deposition geometry, chemical mixing, and defect formation is analyzed using optical and electron microscopy techniques. The results demonstrate that appropriate laser power – scan speed parameter selection is essential to achieve homogeneous mixing between IN718 wire and SS316L powder. Solidification cracking mechanisms associated with Nb-rich microsegregation are demonstrated to follow a consistent interdendritic path through Electron Dispersive X-Ray Spectroscopy elemental maps. While powder addition locally refines dendritic structures and promotes columnar grain partitioning, unfused particles act as nucleation sites that exacerbate defect formation under unstable processing conditions. These findings highlight the critical role of geometric and material input parameters in controlling lack-of-fusion (LoF) defects through improved track overlap. Moreover, the results reveal that as the process transitions to multi-layer configurations, thermal accumulation and repeated remelting dominate defect formation, leading to a shift from LoF to crack-driven mechanisms.

  PublisherDOI

• Physics-Informed Dynamical Modeling of Extrusion-Based Three-Dimensional Printing Processes

  Journal of Dynamic Systems Measurement and Control · 2026-04-07

  articleOpen access

  Abstract The tradeoff between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real-time optimization and control. Although high-fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced-order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier–Stokes equations and further simplified through spatial averaging and input-dependent parameterization. To assess its performance, the model is identified via a nonlinear least-squares approach using computational fluid dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle–substrate gap, and the deposited-layer regions. Overall, the proposed reduced-order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real-time control and optimization.

  PublisherOA PDFDOI

• Thermal Deformation Analysis of Large-Scale High-Aspect-Ratio Parts Fabricated Using Multi-Laser Powder Bed Fusion

  Journal of Experimental and Theoretical Analyses · 2026-01-29

  articleOpen accessSenior author

  Multi-laser powder bed fusion is an emerging additive manufacturing technology that enables the production of high-performance components with intricate geometries and large aspect ratios. These tall, slender structures are highly susceptible to steep thermal gradients and residual stress, leading to deformation that compromises dimensional accuracy and structural integrity. This study investigates how geometric compensation, support structure design, and part scaling influence thermal deformation in Inconel 718 components fabricated via multi-laser powder bed fusion. Using pre-compensation, iterative support refinements, and scaled experimental builds, the deformation response across multiple geometries and print strategies is evaluated. Both compensated and original designs are printed on a commercial system equipped with three simultaneously operating lasers. Results show that printing high-angle surfaces without support structures is infeasible, as thermally induced warping and delamination lead to catastrophic failures. Conical support structures spanning critical regions reduce deformation by more than 50% compared to unsupported builds. Reduced-scale parts, however, do not reliably replicate full-scale deformation behavior due to altered boundary conditions and thermal pathways. These findings highlight the need for integrated design-for-AM workflows where compensation, support design, and scale effects are addressed jointly. The study demonstrates that deformation mechanisms do not scale linearly, emphasizing the limitations of small-scale proxies and the necessity of full-scale validation when developing reliable, deformation-aware design strategies for multi-laser powder bed fusion.

  PublisherOA PDFDOI

• Design of location-specific pin-fin arrays via graph neural networks for enhanced thermal-hydraulic performance

  International Journal of Heat and Mass Transfer · 2026-05-02

  articleSenior author
  PublisherDOI

• Tailoring bead geometry through coaxial powder feeding in wire-based laser-directed energy deposition

  The International Journal of Advanced Manufacturing Technology · 2026-01-08 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Impact of geometric complexity on thermo-hydraulic performance of additively manufactured pin-fin arrays

  Applied Thermal Engineering · 2026-02-27 · 3 citations

  articleSenior authorCorresponding
  PublisherDOI

• TEMPORARY REMOVAL: Effect of strain amplitude on elevated-temperature low-cycle fatigue behavior of Haynes 230 fabricated by laser powder bed fusion

  Materials Characterization · 2026-02-01

  articleSenior authorCorresponding
  PublisherDOI

• Harnessing transfer learning for achieving superior thermal-hydraulic performance in heterogeneous pin-fin arrays

  International Communications in Heat and Mass Transfer · 2025-04-27 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

Frequent coauthors

• Susheel Dharmadhikari

  Pennsylvania State University

  12 shared

• Nandana Menon

  Pennsylvania State University

  12 shared

• Asok Ray

  Pennsylvania State University

  7 shared

• Ritam Pal

  6 shared

• Amit Kumar Ball

  6 shared

• Riddhiman Raut

  5 shared

• Suman Das

  Saha Institute of Nuclear Physics

  5 shared

• Brandon Kemerling

  3 shared

Labs

• PSMES BoardPI

  The PSMES board of directors is made up of elected officers, six to nine at large members, the ME department head, and a mechanical engineering faculty member.