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Integrating Materials and Manufacturing Innovation - Most Downloaded Articles of 2023

1. Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering (this opens in a new tab)

Dennis M. Dimiduk, Elizabeth A. Holm, Stephen R. Niezgoda

2. In-process sensing in selective laser melting (SLM) additive manufacturing (this opens in a new tab)

Thomas G. Spears, Scott A. Gold

3. DREAM.3D: A Digital Representation Environment for the Analysis of Microstructure in 3D (this opens in a new tab)

Michael A Groeber, Michael A Jackson

4. Microstructure Characterization and Reconstruction in Python: MCRpy (this opens in a new tab)

Paul Seibert et al.

5. Metal additive-manufacturing process and residual stress modeling (this opens in a new tab)

Mustafa Megahed et al.

6. OpenCalphad - a free thermodynamic software (this opens in a new tab)

Bo Sundman, Ursula R Kattner, Mauro Palumbo, Suzana G Fries

7. ITKMontage: A Software Module for Image Stitching (this opens in a new tab)

Dženan Zukić et al.

8. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters (this opens in a new tab)

Ankit Agrawal et al.

9. Bi-directional Scan Pattern Effects on Residual Stresses and Distortion in As-built Nitinol Parts: A Trend Analysis Simulation Study (this opens in a new tab)

Medad C. C. Monu et al.

10. Symmetric and asymmetric tilt grain boundary structure and energy in Cu and Al (and transferability to other fcc metals) (this opens in a new tab)

Mark A. Tschopp, Shawn P. Coleman, David L. McDowell

11. Machine Learning Prediction of Heat Capacity for Solid Inorganics (this opens in a new tab) Steven K. Kauwe, Jake Graser, Antonio Vazquez, Taylor D. Sparks
12. Random Generation of Lattice Structures with Short-Range Order (this opens in a new tab) Lauren T. W. Fey, Irene J. Beyerlein
13. Quantitative Benchmarking of Acoustic Emission Machine Learning Frameworks for Damage Mechanism Identification (this opens in a new tab) C. Muir et al.
14. Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing (this opens in a new tab) Scott Howland et al.
15. On the Fidelity of the Scaling Laws for Melt Pool Depth Analysis During Laser Powder Bed Fusion (this opens in a new tab) M. Naderi, J. Weaver, D. Deisenroth, N. Iyyer, R. McCauley
16. Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery (this opens in a new tab) Kareem S. Aggour et al.
17. CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry (this opens in a new tab) Anthony Yu-Tung Wang et al.
18. A Novel Methodology for the Thermographic Cooling Rate Measurement during Powder Bed Fusion of Metals Using a Laser Beam (this opens in a new tab) David L. Wenzler et al.
19. Residual Strain Predictions for a Powder Bed Fusion Inconel 625 Single Cantilever Part (this opens in a new tab) Yangzhan Yang, Madie Allen, Tyler London, Victor Oancea
20. A Comparison of Statistically Equivalent and Realistic Microstructural Representative Volume Elements for Crystal Plasticity Models (this opens in a new tab) Fatemeh Azhari et al.
21. PRISMS-Plasticity TM: An Open-Source Rapid Texture Evolution Analysis Pipeline (this opens in a new tab) Mohammadreza Yaghoobi, John E. Allison, Veera Sundararaghavan
22. Overview of Additive Manufacturing Informatics: “A Digital Thread” (this opens in a new tab) Deborah Mies, Will Marsden, Stephen Warde
23. On the Prediction of Uniaxial Tensile Behavior Beyond the Yield Point of Wrought and Additively Manufactured Ti-6Al-4V (this opens in a new tab) Maria J. Quintana, Andrew J. Temple, D. Gary Harlow, Peter C. Collins
24. Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design (this opens in a new tab) Yuwei Mao et al.
25. Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing (this opens in a new tab) Yue Chen et al.

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