Manufacturing Letters

Aims & Scope

Manufacturing Letters (MFGLET) is an online, rapid-publication journal providing a home for short, high-quality papers from the international academic and industry community on important advances from all interdisciplinary research areas impacting manufacturing.

The journal promotes an exchange of ideas and communicates significant developments of immediate interest to others engaged in formative research that contributes to progress in manufacturing techniques, models, processes, and systems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2

Journal Rank

Year	Value
2024	10568

Journal Citation Indicator

Year	Value
2024	1418

SJR (SCImago Journal Rank)

Year	Value
2024	0.551

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	46

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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, 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.

• A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

  Citation: 3602

  Authors: Jay, Behrad, Hung-An

• Recent advances and trends in predictive manufacturing systems in big data environment

  Citation: 772

  Authors: Jay, Edzel, Behrad, Hung-an

• Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

  Citation: 600

  Authors: Jay, Hossein, Jaskaran, Vibhor

• Digital twin – Proof of concept

  Citation: 425

  Authors: Sebastian, Reiner

• A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing

  Citation: 242

  Authors: H., T.

• A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems

  Citation: 225

  Authors: Jay, Moslem, Jaskaran

• Framework for a digital twin in manufacturing: Scope and requirements

  Citation: 212

  Authors: Guodong, Moneer

• Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning

  Citation: 201

  Authors: Zeqing, Zhizhou, Grace X.

• Towards a cyber-physical-social-connected and service-oriented manufacturing paradigm: Social Manufacturing

  Citation: 181

  Authors: Pingyu, Kai, Jiewu

• Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data

  Citation: 176

  Authors: Jack, Linkan

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