JULY 2024
Materials data science using CRADLE: A distributed, data-centric approach
Thomas G. Ciardi, Arafath Nihar, Rounak Chawla, Olatunde Akanbi, Pawan K. Tripathi, Y. Wu, V. Chaudhary, and R. H. French, “Materials data science using CRADLE: A distributed, data-centric approach,” MRS Communications, Jul. 2024, https://doi.org/10.1557/s43579-024-00616-6
L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration
K. J. Hernandez, T. G. Ciardi, R. Yamamoto, M. Lu, A. Nihar, J. C. Jimenez, P. K. Tripathi, B. Giera, J.-B. Forien, J. J. Lewandowski, R. H. French, and L. S. Bruckman, “L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration,” Integrating Materials and Manufacturing Innovation, Jul. 2024, https://doi.org/10.1007/s40192-024-00368-0
JUNE 2024
Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue
A. Ngo, K. Hernandez, A. E. Olatunde, T. G. Ciardi, A. Harding, A. Nifar, A. Mondal, R. H. French, L. S. Bruckman, and J. J. Lewandowski, “Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue,” JOM, Jun. 2024, https://doi.org/10.1007/s11837-024-06655-7
MAY 2024
Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations
W. Yue, M. R. Mehdi, P. K. Tripathi, M. A. Willard, F. Ernst, and R. H. French, “Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations,” MRS Advances, May 2024, https://doi.org/10.1557/s43580-024-00862-9
A Data Integration Framework of Additive Manufacturing Based on Fair Principles
Kristen J. Hernandez, Erika I. Barcelos, Jayvic C. Jimenez, Arafath Nihar, Pawan K. Tripathi, Brian Giera, Roger H. French, and Laura S. Bruckman, “A Data Integration Framework of Additive Manufacturing Based on Fair Principles,” MRS Advances, May 2024, https://doi.org/10.1557/s43580-024-00874-5
APRIL 2024
Towards a study protocol: A data-driven workflow to identify error sources in direct ink write mechatronics
H. H. Aung, J. C. Jimenez, B. Au, P. Caviness, R. Cerda, Q. D. Tran, P. Tripathi, B. Giera, R. H. French, and L. S. Bruckman, “Towards a study protocol: A data-driven workflow to identify error sources in direct ink write mechatronics,” MRS Advances, Apr. 2024, https://doi.org/10.1557/s43580-024-00846-9
MARCH 2024
Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning
Yue, W., Tripathi, P.K., Ponon, G. et al. Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning. Integrating Materials and Manufacturing Innovation 13, 36–52 (2024). https://doi.org/10.1007/s40192-023-00328-0
A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences
S. Nalin Venkat, T. G. Ciardi, M. Lu, P. C. DeLeo, J. Augustino, A. Goodman, J. C. Jimenez, A. Mondal, F. Ernst, C. A. Orme, Y. Wu, R. H. French, and L. S. Bruckman, “A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences,” Integrating Materials and Manufacturing Innovation, vol. 13, no. 1, pp. 71–82, Mar. 2024, https://doi.org/10.1007/s40192-024-00342-w
dECEMBER 2023
Accelerating Time to Science using CRADLE: A Framework for Materials Data Science
Arafath Nihar, Thomas Ciardi, Rounak Chawla, Olatunde D. Akanbi, Vipin Chaudhary, Yinghui Wu, and Roger H. French. “Accelerating Time to Science Using CRADLE: A Framework for Materials Data Science.” Goa, India: IEEE, 2023. https://doi.org/10.1109/HiPC58850.2023.00041.
Materials Data Science Using CRADLE: A Distributed, Data-centric Approach
Thomas G. Ciardi, Arafath Nihar, Rounak Chawla, Pawan K. Tripathi, Yinghui Wu, Vipin Chaudhary, and Roger H. French. “Materials Data Science Using CRADLE: A Distributed, Data-Centric Approach.” MRS Communications, December 11, 2023.
Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images
Lu, Mingjian, Sameera Nalin Venkat, Jube Augustino, David Meshnick, Jayvic Cristian Jimenez, Pawan K. Tripathi, Arafath Nihar, et al. “Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images.” Integrating Materials and Manufacturing Innovation 12, no. 4 (December 1, 2023): 371–85. https://doi.org/10.1007/s40192-023-00320-8.
Enhancing Scientific Image Classification through Multimodal Learning: Insights from Chest X-Ray and Atomic Force Microscopy Datasets
Meshnick, David C., Nahal Shahini, Debargha Ganguly, Yinghui Wu, Roger H. French, and Vipin Chaudhary. “Enhancing Scientific Image Classification through Multimodal Learning: Insights from Chest X-Ray and Atomic Force Microscopy Datasets.” In 2023 IEEE International Conference on Big Data (BigData), 2211–20, 2023. https://doi.org/10.1109/BigData59044.2023.10386478.
