2026

  • Dalezios, I., Wong, G.D., Nguyen-Minh, T. et al. Novel Quantification of Non-convex Grain Morphology in Printed Ti–6Al–4V. Metall Mater Trans A 57, 2511–2524 (2026). https://doi.org/10.1007/s11661-026-08159-2
  • Dong, H. Al Jame, Z. C. Cordero, and S. M. Taheri-Mousavi, Computational predictions of complex property trajectories in compositionally graded alloys, Additive Manufacturing Letters, 17, (2026); doi:10.1016/j.addlet.2026.100359.
  • Jame, Hasan Al and Dong, Jixuan and Karve, Pranav and Mahadevan, Sankaran and Taheri-Mousavi, S. Mohadeseh, Certification-aware uncertainty-guided inverse design of additively manufactured alloys. Available at SSRN: https://ssrn.com/abstract=6954541or http://dx.doi.org/10.2139/ssrn.6954541
  • Al Jame, J. Shao, J. Dong, P. Karve, K. Mumm, B. A. Webler, A. D. Rollett, S. Mahadevan, T. Sun, and S. M. Taheri-Mousavi, Global sensitivity analysis for microstructural features to variability in elemental concentration of additively manufactured alloy 718, Materialia, 45, (2026); doi:10.1016/j.mtla.2025.102630.
  • P. Logakannan, M. Aristizabal, G. Bomarito, Z. Xu, S. Zhe, R. M. Kirby, H. Millwater, and J. Hochhalter, Efficiently training SciML models with derivative-informed training data using order truncated imaginary numbers, Computer Methods in Applied Mechanics and Engineering, 452, (2026); doi:10.1016/j.cma.2026.118789.
  • Ley, A. Q. Ngo, and J. J. Lewandowski, Dataset on Fatigue Results and Fatigue Fracture Initiation Site Characterization in Stress-Relieved PBF-LB/M Ti-6Al-4V Four-Point Bend and Axial Specimens: Part I (High Power, Variable Scan Velocities), DATA, 11, (2026); doi:10.3390/data11040081.
  • Miner, J.P., Hobdari, M., Stanton, H. et al. Impact of Segmentation Methods on Predicting Fatigue-Initiating Pores from X-ray Computed Tomography Data. Integr Mater Manuf Innov (2026). https://doi.org/10.1007/s40192-026-00467-0
  • Senthilnathan and S. Mahadevan, Uncertainty quantification of Ti-7Al microstructure features and mechanical properties, Computational Materials Science, 271, (2026); doi:10.1016/j.commatsci.2026.114769.
  • J. F. Templeton, J. A. McCauley, M. Khrenov, S. Hinnebusch, M. Pena, L. Shao, A. C. To, and S. P. Narra, Property optimization through full-part thermal history control in laser powder bed fusion additive manufacturing, Additive Manufacturing, 123, (2026); doi:10.1016/j.addma.2026.105205. 

2025

  • Evans, M.L., Rignanese, GM., Elbert, D. et al. Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences. MRS Bulletin 50, 838–845 (2025). https://doi.org/10.1557/s43577-025-00925-8
  • P. Miner and S. P. Narra, Extreme value statistics with uncertainty to assess porosity equivalence across additively manufactured parts, Reliability Engineering & System Safety, 262, (2025); doi:10.1016/j.ress.2025.111207.
  • Montalbano, T.; Nimer, S.; Daffron, M.; Croom, B.; Ghosh, S.; Storck, S. Machine Learning Enabled Discovery of New L-PBF Processing Domains for Ti-6Al-4V. Additive Manufacturing, 2025, 98, 104632. https://www.sciencedirect.com/science/article/pii/S221486042400678X
  • Brodan Richter, Joshua D. Pribe, George R. Weber, Vamsi Subraveti, Caglar Oskay,
    Analytical prediction of lack-of-fusion porosity including uncertainty and variable melt pools for powder bed fusion, Additive Manufacturing, Volume 103, 2025, 104733, ISSN 2214-8604, https://doi.org/10.1016/j.addma.2025.104733.
  • Richter, B., Pribe, J.D., Weber, G.R., Subraveti, V., & Oskay, C. (2025). Analytical prediction of lack-of-fusion porosity including uncertainty and variable melt pools for powder bed fusion. Additive Manufacturing. https://www.sciencedirect.com/science/article/pii/S2214860425000971
  • J.S. Rincon-Tabares, M. Aristizabal, A. Montoya, H. Millwater, and D. Restrepo. “Sensitivity Analysis for the Study and Enhancement of Powder Bed Fusion of Metals via Computational Models and Hypercomplex Automatic Differentiation.” Submitted to Journal of Materials Research and Technology.
  • Subraveti, V., Richter, B., Pribe, J.D. et al. Process Uncertainty Analysis of Stochastic Lack-of-Fusion Defects in Laser Powder Bed Fused Inconel 718. Integr Mater Manuf Innov (2025). https://doi.org/10.1007/s40192-025-00421-6
  • Subraveti, V., Richter, B., Pribe, J.D. et al. Process Uncertainty Analysis of Stochastic Lack-of-Fusion Defects in Laser Powder Bed Fused Inconel 718. Integr Mater Manuf Innov (2025). https://doi.org/10.1007/s40192-025-00421-6
  • C. Velasquez-Gonzalez, M. Aristizabal, J. D. Navarro, H. R. Millwater, and D. Restrepo, Efficient and Accurate Computation of Arbitrary-Order Eigenpair Sensitivities Using Hypercomplex Automatic Differentiation, International Journal for Numerical Methods In Engineering, 126, (2025); doi:10.1002/nme.70245.
  • C. Velasquez-Gonzalez, J. D. Navarro, M. Aristizabal, H. Millwater, and D. Restrepo, Derivative-enhanced Bayesian optimization for broad-bandgap phononic metamaterials with hypercomplex automatic differentiation, Finite Elements in Analysis and Design, 252, (2025); doi:10.1016/j.finel.2025.104461.
  • Nathan A. Wassermann, Justin P. Miner, Jiayun Shao, Tao Sun, Alan J.H. McGaughey, Sneha Prabha Narra, Evolution of powder-entrapped pores in Ti–6Al–4V fabricated with powder bed fusion-laser beam process, Additive Manufacturing, Volume 109, 2025, 104838, ISSN 2214-8604, https://doi.org/10.1016/j.addma.2025.104838
  • Wu, A. D. Rollett, and A. Mostafaei, Fatigue behavior of low-cost, non-spherical Ti-6Al-4V powder processed via laser powder bed fusion, International Journal Of Advanced Manufacturing Technology, 137, 5177-5183 (2025); doi:10.1007/s00170-025-15380-7.
  • Yu-Tsen Yi, Junwon Seo, Kevin Murphy & Anthony Rollett. Rapid Grain Segmentation of Heat-treated and Annealed LPBF Haynes 282 Using an Unsupervised Learning-Based Computer Vision Approach. Integr Mater Manuf Innov 14, 75–88 (2025). https://doi.org/10.1007/s40192-024-00390-2.

2024

  • D. Furrer, S. Ghosh, A. Rollett, S. Burlatsky, and M. Anahid, Model-Based Material and Process Definitions for Additive Manufactured Component Design and Qualification, Integrating Materials and Manufacturing Innovation, 13, 488-510 (2024). https://doi.org/10.1007/s40192-024-00358-2
  • Andrew Huck, Amit Verma, Katie O'Donnell, Lonnie Smith, Venkata Satya Surya Amaranth Karra, Ali Guzel, Hangman Chen, P. Chris Pistorius, Bryan Webler, and Anthony Rollett, “Location-dependent phase transformation kinetics during laser wire deposition additive manufacturing of Ti-6Al-4V,” Metallurgical and Materials Transactions A. https://link.springer.com/article/10.1007/s11661-024-07567-6
  • J. P. Miner, A. Ngo, C. Gobert, T. Reddy, J. J. Lewandowski, A. D. Rollett, J. Beuth, and S. P. Narra, 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, 95, (2024); doi:10.1016/j.addma.2024.104506.
  • Miner, J.P. and Narra, S.P. Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications. arXiv. https://arxiv.org/abs/2411.03401
  • A. Ngo et al., Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue, https://doi.org/10.1007/s11837-024-06655-7, JOM, pp. 1-12, 2024.
  • A. Olleak, E. Adcock, S. Hinnebusch, F. Dugast, A. D. Rollett, and A. C. To, Understanding the role of geometry and interlayer cooling time on microstructure variations in LPBF Ti-6Al-4V through part-scale scan-resolved thermal modeling, Additive Manufacturing Letters, 9, (2024); doi:10.1016/j.addlet.2024.100197.
  • Reddy, T., Ngo, A., Miner, J.P., Gobert, C., Beuth, J.L., Rollett, A.D., Lewandowski, J.J., and Narra, S.P. (2024), Fatigue-Based Process Window for Laser Beam Powder Bed Fusion Additive Manufacturing, https://doi.org/10.1016/j.ijfatigue.2024.108428, International Journal of Fatigue, 187, 108428s.
  • Z. Ren, J. Shao, H. Liu, S. J. Clark, L. Gao, L. Balderson, K. Mumm, K. Fezzaa, A. D. Rollett, L. B. Kara, and T. Sun, Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning, Materials Futures, 3, 045001 (2024); doi:10.1088/2752-5724/ad89e2.
  • J.S. Rincon-Tabares, M. Aristizabal, M. Balcer, A. Montoya, H. Millwater, and D. Restrepo, Efficient Sensitivity Analysis of the Thermal Profile in Powder Bed Fusion of Metals Using Hypercomplex Automatic Differentiation Finite Element Method, Submitted to Additive Manufacturing. Preprint available at SSRN 4854046. https://www.sciencedirect.com/science/article/pii/S2214860424005347?ssrnid=4854046&dgcid=SSRN_redirect_SD
  • V. Tari, C. Kantzos, B. S. Anglin, and A. D. Rollett, Analytical verification of FFT-based micromechanical simulations near an elliptical crack tip by using composite voxels, Mechanics of Advanced Materials and Structures, 31, 10859-10868 (2024); doi:10.1080/15376494.2023.2298233.
  • Vamsi Subraveti, Brodan Richter, Saikumar R. Yeratapally, Caglar Oskay, Three-Dimensional Prediction of Lack-of-Fusion Porosity Volume Fraction and Morphology for Powder Bed Fusion Additively Manufactured Ti-6Al-4V. IMMI. https://link.springer.com/article/10.1007/s40192-024-00347-5

2023

  • S. Ghosh, D. Dimiduk, and D. Furrer, Statistically equivalent representative volume elements (SERVE) for material behavior analysis and multiscale modeling, International Materials Reviews, 68, 1158-1191 (2023); doi:10.1080/09506608.2023.2246766.
  • Z. Ren, L. Gao, S. J. Clark, K. Fezzaa, P. Shevchenko, A. Choi, W. Everhart, A. D. Rollett, L. Chen, and T. Sun, Machine learning--aided real-time detection of keyhole pore generation in laser powder bed fusion, Science, 379, 89--94 (2023); doi:10.1126/science.add4667.

2022

  • M. Pinz, S. Storck, T. Montalbano, B. Croom, N. Salahuddin, M. Trexler and S. Ghosh, “Efficient computational framework for image-based micromechanical analysis of additively manufactured Ti-6Al-4V Alloy”, Additive Manufacturing, Vol. 60, Part A, Art. 103269, December 2022.  https://doi.org/10.1016/j.addma.2022.103269
  • M. Pinz, J.T. Benzing, A. Pilchak, and S. Ghosh, “A microstructure-based porous crystal plasticity FE model for additively manufactured Ti-6Al-4V alloys”, International Journal of Plasticity, Vol. 153, 103254, June 2022. https://doi.org/10.1016/j.ijplas.2022.103254