Journal of Intelligent Manufacturing and Special Equipment

Aims & Scope

The Journal of Intelligent Manufacturing and Special Equipment aims to provide a platform for researchers and practitioners to share knowledge and innovations in the field of intelligent manufacturing and special equipment.

The journal focuses on the development and application of intelligent manufacturing systems, technologies, and specialized equipment, with a particular emphasis on automation, robotics, quality control, and supply chain management.

By publishing high-quality research papers, case studies, and reviews, the journal aims to advance the state-of-the-art in intelligent manufacturing and special equipment, and to provide a valuable resource for professionals, academics, and policymakers in the field.

Abstracting & Indexing

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

• Directory of Open Access Journals (DOAJ)
• Google Scholar

Subjects & Keywords

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

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Apc url
• Homepage
• Aims scope

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Chemical Engineering and Computer Science, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies. Learn more.

Explore journals without APCs for alternative publishing options.

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.

• Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges

  Citation: 103

  Authors: Weifei, Tongzhou, Xiaoyu, Zhenyu, Jianrong

• Comparative study on equivalent models calculating magnetic force between permanent magnets

  Citation: 12

  Authors: Yuyang, Yonggang, Hao, Xukun, Shuailing, Xiaoyu, Junjie

• MFCT-GAN: multi-information network to reconstruct CT volumes for security screening

  Citation: 11

  Authors: Yixiang

• Aeronautical composite/metal bolted joint and its mechanical properties: a review

  Citation: 8

  Authors: Qinglong, Chenguang, Tai, Fan, Zhilei, Entao, Ende, Ming

• Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales

  Citation: 8

  Authors: Peiqing, Huile, Zixiao, Kanglong, Qipeng

• A coordinated control strategy for battery/supercapacitor hybrid energy storage system to eliminate unbalanced voltage in a standalone AC microgrid

  Citation: 6

  Authors: Yaxing, Saqib Jamshed, Lin

• Artificial intelligence in intelligent transportation systems

  Citation: 6

  Authors: Leila

• The multi-objective optimization of tunneling boring machine control based on geological conditions identification

  Citation: 5

  Authors: Hongyuan, Jingcheng

• Ultrasonic vibration cutting of advanced aerospace materials: a critical review of in-service functional performance

  Citation: 4

  Authors: Zhenlong, Aowei, Chenlin, Hongru, Xiangyu

• Lateral control of intelligent vehicles using radial basis function neural networks with sliding mode control based on fractional order calculus

  Citation: 3

  Authors: Peiqing, Taiping, Hao, Lijun, Qipeng