Journal of Intelligent Manufacturing

JournalCitation impact & published research

Journal of Intelligent Manufacturing is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 3,682 works, 128,642 citations, an h-index of 139 and an APC (USD) of 2,990.

3,682

Works

128,642

Citations

139

h-index

2,990

APC (USD)

IDs:OpenAlex

What are the most-cited papers on Journal of Intelligent Manufacturing?

ScholarIQmost cited works

Modular and platform methods for product family design: literature analysis

José Alberto, Michel Tollenaere

Journal of Intelligent Manufacturing. 2005296 Citations

An approach to monitoring quality in manufacturing using supervised machine learning on product state data

Thorsten Wuest, Christopher Irgens, Klaus‐Dieter Thoben

Journal of Intelligent Manufacturing. 2013243 Citations

From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0

Riccardo Rosati, Luca Romeo, Gianalberto Cecchini, F. Tonetto, Paolo Viti, Adriano Mancini, Emanuele Frontoni

Journal of Intelligent Manufacturing. 2022238 CitationsOPEN ACCESS

Industrial wearable system: the human-centric empowering technology in Industry 4.0

Xiang T.R. Kong, Hao Luo, George Q. Huang, Xuan Yang

Journal of Intelligent Manufacturing. 2018207 Citations

A new paradigm of cloud-based predictive maintenance for intelligent manufacturing

Jinjiang Wang, Laibin Zhang, Lixiang Duan, Robert X. Gao

Journal of Intelligent Manufacturing. 2015196 Citations

Related on ScholarIQ

Modular and platform methods for product family design: literature analysis

Paper

An approach to monitoring quality in manufacturing using supervised machine learning on product state data

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From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0

Paper

Industrial wearable system: the human-centric empowering technology in Industry 4.0

Paper

A new paradigm of cloud-based predictive maintenance for intelligent manufacturing

Paper

Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

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