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Machines
Special Issues
Smart Manufacturing and Industrial Automation, 2nd Edition
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Smart Manufacturing and Industrial Automation, 2nd Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 807

Special Issue Editor

Prof. Dr. Nand Jha

E-Mail Website
Guest Editor
Mechanical Engineering Department, Manhattan College, New York 10471, NY, USA
Interests: robotics; automation; manufacturing sciences; smart manufacturing; ML and AI in manufacturing; data analysis in smart manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the Special Issue “Smart Manufacturing and Industrial Automation” (https://www.mdpi.com/journal/machines/special_issues/79874L8322), we are pleased to announce the next in the series, entitled “Smart Manufacturing and Industrial Automation, 2nd Edition”.

The international journal Machines is publishing this Special Issue, which invites submissions on multidisciplinary approaches to smart manufacturing. We welcome theories on the development of digitization in manufacturing processes, including ML and AI in manufacturing.

Industrial automation via the use of the Internet of Things (IoT) has become an incredibly popular topic in manufacturing. Automation is driving the positive changes seen in customer experience, offering convenience, speed, and quality service, which make this topic contemporary and industrially relevant. The aim and objective of smart manufacturing is to provide low-cost solutions for end-to-end visibility in terms of manufacturing processes. The manufacturing industry is changing rapidly with the application of industrial automation such as ML and AI, meaning that this may be an opportune time to discuss these issues under the broad umbrella of smart manufacturing. Smart manufacturing is a flexible and software-driven IoT platform that can be used in any location. Authors interested in submitting an article to this Special Issue are encouraged to select any topic of their choice from the list of options below.

Topics for this Special Issue include, but are not limited to, the following:

  • Reviews of smart manufacturing research, the industry, and the future of smart manufacturing.
  • Digitization in the automation of manufacturing.
  • AI and ML in manufacturing.
  • Industrial automation and Industry 4.0.
  • The Industrial Internet of Things (IIOT) and its practices.
  • The design of sensors for smart manufacturing.
  • Industrial automation and robotics for Industry 4.0.
  • Automation, the IIOT, and sustainability.
  • Data acquisition and data analyses in smart manufacturing.
  • Growing cyber-security for smart manufacturing.
  • Industry 4.0 and fog and cloud computing.
  • Software for Industry 4.0 and smart manufacturing.

Prof. Dr. Nand Jha
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart manufacturing
  • industrial automation and robotics
  • industrial Internet of Things (IIOT)
  • data acquisition and data analyses
  • industry 4.0

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Related Special Issue

Published Papers (1 paper)

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Research

20 pages, 1536 KB  
Article
Contrastive Learning-Based One-Class Classification for Intelligent Manufacturing System
by Seunghwan Song
Machines 2025, 13(12), 1109; https://doi.org/10.3390/machines13121109 - 1 Dec 2025
Viewed by 354
Abstract
Time-series anomaly detection is imperative for ensuring reliability and safety in intelligent manufacturing systems. However, real-world environments typically provide only normal operating data and exhibit significant periodicity, noise, imbalance, and domain variability. The present study proposes CL-OCC, a contrastive learning-based one-class framework that [...] Read more.
Time-series anomaly detection is imperative for ensuring reliability and safety in intelligent manufacturing systems. However, real-world environments typically provide only normal operating data and exhibit significant periodicity, noise, imbalance, and domain variability. The present study proposes CL-OCC, a contrastive learning-based one-class framework that integrates seasonal-trend decomposition using loess (STL) for structure-preserving temporal augmentation, a cosine-regularized soft boundary for compact normal-region formation, and variance-preserving regularization to prevent latent collapse. A convolutional recurrent encoder is first pretrained via an autoencoder objective and subsequently optimized through a unified loss that balances contrastive invariance, soft-boundary constraint, and variance dispersion. Experiments on semiconductor equipment data and three public benchmarks demonstrate that CL-OCC provides competitive or superior performance relative to reconstruction-, prediction-, and contrastive-based baselines. CL-OCC exhibits smoother anomaly trajectories, earlier detection of gradual drifts, and strong robustness to noise, window-length variation, and extreme class imbalance. A study of the effects of ablation and interaction on the stability of representations indicates that STL-based augmentation, boundary shaping, and variance regularization contribute complementary benefits to this stability. While the qualitative results indicate limited sensitivity to extremely short impulsive disturbances, the proposed framework delivers a generalizable and stable solution for unsupervised industrial monitoring, with promising potential for extension to multi-resolution analysis and online prognostics and health management (PHM) applications. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation, 2nd Edition)
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