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Artificial Intelligence for Industrial Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 478

Special Issue Editors


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Guest Editor
Institute of Information Science, University of Miskolc, 3515 Miskolc, Hungary
Interests: computer science; information technology; artificial intelligence; blockchain

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Guest Editor
Institute of Information Science, University of Miskolc, 3515 Miskolc, Hungary
Interests: software bug detection and prediction

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing the field of industrial informatics by enabling intelligent decision-making, predictive analytics, and automation across a wide range of industrial applications. This Special Issue aims to explore the latest research, developments, and practical implementations of AI in industrial environments, focusing on machine learning, deep learning, computer vision, natural language processing, intelligent data analysis, system modeling,  maintenance, logistics, predictive decision-making in industrial environments, and process control.

Topics of interest include, but are not limited to, the following:

  • Predictive maintenance and fault detection;
  • Industrial process optimization;
  • Intelligent control systems and automation;
  • Cyber–physical production systems and digital twins;
  • AI for industrial robotics and human–machine collaboration;
  • Anomaly detection in smart manufacturing;
  • AI-driven industrial cybersecurity;
  • AI-based quality assurance and inspection;
  • Applications of LLMs in industrial informatics;
  • Case studies with smart services and platforms;
  • Explainability and transparency in industrial AI applications.

Submissions that demonstrate real-world applications, interdisciplinary approaches, or experimental validation in industrial settings are particularly welcome.

Dr. Olivér Hornyák
Dr. Károly Nehéz
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • artificial intelligence
  • industrial informatics
  • fault detection
  • smart manufacturing

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Published Papers (1 paper)

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Research

18 pages, 27194 KB  
Article
A Synthetic Image Generation Pipeline for Vision-Based AI in Industrial Applications
by Nishanth Nandakumar and Jörg Eberhardt
Appl. Sci. 2025, 15(23), 12600; https://doi.org/10.3390/app152312600 - 28 Nov 2025
Abstract
The collection and annotation of large-scale image datasets remains a significant challenge in training vision-based AI models, especially in domains such as industrial automation. In industrial settings, this limitation is especially critical for quality inspection tasks within Flexible Manufacturing Systems and Batch-Size-of-One production, [...] Read more.
The collection and annotation of large-scale image datasets remains a significant challenge in training vision-based AI models, especially in domains such as industrial automation. In industrial settings, this limitation is especially critical for quality inspection tasks within Flexible Manufacturing Systems and Batch-Size-of-One production, where high variability in components restricts the availability of relevant datasets. This study presents a pipeline for generating photorealistic synthetic images to support automated visual inspection. Rendered images derived from geometric models of manufactured parts are enhanced using a Cycle-Consistent Adversarial Network (CycleGAN), which transfers pixel-level features from real camera images. The pipeline is applied in two scenarios: (1) domain transfer between similar objects for data augmentation, and (2) domain transfer between dissimilar objects to synthesize images before physical production. The generated images are evaluated using mean Average Precision (mAP) and the Turing test, respectively. The pipeline is further validated in two industrial setups: object detection for a pick-and-place task using a Niryo robot, and anomaly detection in products manufactured by a FESTO machine. The successful implementation of the pipeline demonstrates its potential to generate effective training data for vision-based AI in industrial applications and highlights the importance of enhancing domain quality in industrial synthetic data workflows. Full article
(This article belongs to the Special Issue Artificial Intelligence for Industrial Informatics)
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