Smart and Sustainable Manufacturing Systems

Aims & Scope

This journal fosters transdisciplinary research that crosses the boundaries of information science, systems engineering and engineering design, manufacturing, data science, and product life cycle with a focus on how to make manufacturing systems smarter and sustainable.

The journal specifically invites papers that will address advanced manufacturing; energy and materials for manufacturing; product, process, and asset (machine tools, machinery) digitization for integrated production optimization; measurement science; and standards, protocols, and tools needed to design, analyze, and control smart manufacturing systems based on a cyber-physical infrastructure for manufacturing.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.6
2024	0.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.252

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	14

Journal Rank

Year	Value
2024	18526

Journal Citation Indicator

Year	Value
2024	64

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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 Computer Science and Engineering, 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.

• Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning

  Citation: 203

  Authors: Max, Ronay, Yung-Tsun Tina, Kincho H.

• Toward a Digital Thread and Data Package for Metals-Additive Manufacturing

  Citation: 47

  Authors: D. B., P., Y., S.

• Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

  Citation: 43

  Authors: J., D., R., M., K. H., Y.-T. T., S.

• Guidance on the Use of Existing ASTM Polymer Testing Standards for ABS Parts Fabricated Using FFF

  Citation: 35

  Authors: Arielle, Celeste, Grant

• A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics

  Citation: 27

  Authors: Y. T., S., S., S., M., Q. Y., S., D., C. J., S.

• SMART: A System-Level Manufacturing and Automation Research Testbed

  Citation: 26

  Authors: Ilya, Miguel, Kira, Dawn

• Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals

  Citation: 24

  Authors: Jacob, Paul, Adam, Prahalada, Ashok

• In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning

  Citation: 24

  Authors: Aniruddha, Farhad, Hui, Edward, Prahalada

• Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations

  Citation: 21

  Authors: Dy D., Vung, Huyen N., Tommy

• Reliability of C-H-O Symbiosis Networks under Source Streams Uncertainty

  Citation: 19

  Authors: Rajib, Mahmoud M.

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