Integrating Materials and Manufacturing Innovation

Aims & Scope

The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.5
2024	2.40

Journal Rank

Year	Value
2024	8132

Journal Citation Indicator

Year	Value
2024	419

SJR (SCImago Journal Rank)

Year	Value
2024	0.698

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	37

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Engineering and Materials Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• DREAM.3D: A Digital Representation Environment for the Analysis of Microstructure in 3D

  Citation: 845

  Authors: Michael A, Michael A

• In-process sensing in selective laser melting (SLM) additive manufacturing

  Citation: 387

  Authors: Thomas G., Scott A.

• Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

  Citation: 273

  Authors: Dennis M., Elizabeth A., Stephen R.

• Metal additive-manufacturing process and residual stress modeling

  Citation: 223

  Authors: Mustafa, Hans-Wilfried, Narcisse, Hongzhi, Olivier

• Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

  Citation: 222

  Authors: Ankit, Parijat D, Ahmet, Gautham P, Alok N, Surya R

• Development and application of MIPARâ„¢: a novel software package for two- and three-dimensional microstructural characterization

  Citation: 187

  Authors: John M, Daniel E, Brian, Hamish L

• Symmetric and asymmetric tilt grain boundary structure and energy in Cu and Al (and transferability to other fcc metals)

  Citation: 162

  Authors: Mark A., Shawn P., David L.

• High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates

  Citation: 154

  Authors: Julia, Maxwell, Erin, Sean, Bryce

• OpenCalphad - a free thermodynamic software

  Citation: 144

  Authors: Bo, Ursula R, Mauro, Suzana G

• Benchmark Study of Thermal Behavior, Surface Topography, and Dendritic Microstructure in Selective Laser Melting of Inconel 625

  Citation: 135

  Authors: Zhengtao, Yanping, Stephen E., Kevontrez K., Wing Kam, Gregory J.

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