Integrating Materials and Manufacturing Innovation

Papers
(The H4-Index of Integrating Materials and Manufacturing Innovation is 15. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-05-01 to 2025-05-01.)
ArticleCitations
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing48
VAMPYR: A MATLAB-Based Toolset Leveraging MTEX for Automating VPSC33
Multi-physics Approach to Predict Fatigue Behavior of High Strength Aluminum Alloy Repaired via Additive Friction Stir Deposition31
PRISMS-Indentation: Multi-scale Elasto-Plastic Virtual Indentation Module29
Computational Alloy Design for Process-Related Uncertainties in Powder Metallurgy26
An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models21
A Common Data Dictionary and Common Data Model for Additive Manufacturing19
Natural Language Processing-Driven Microscopy Ontology Development18
MAUD Rietveld Refinement Software for Neutron Diffraction Texture Studies of Single- and Dual-Phase Materials18
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design18
Effects of Boundary Conditions on Microstructure-Sensitive Fatigue Crystal Plasticity Analysis17
Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing16
New Paradigms in Model Based Materials Definitions for Titanium Alloys in Aerospace Applications16
Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges15
Microstructure Characterization and Reconstruction in Python: MCRpy15
The AFRL Additive Manufacturing Modeling Challenge: Predicting Micromechanical Fields in AM IN625 Using an FFT-Based Method with Direct Input from a 3D Microstructural Image15
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