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Applied Sciences
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New Technologies in Intelligent Manufacturing and Industrial Engineering, 2nd Edition
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New Technologies in Intelligent Manufacturing and Industrial Engineering, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 929

Special Issue Editors

Dr. Daniel Jancarczyk

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Guest Editor
Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland
Interests: design, analysis and diagnostics of industrial control systems; performing safety audits of machines and devices; designing industrial vision systems using artificial intelligence algorithms; improvement of diagnostic systems enabling assessment of the condition of oil-paper insulation of power transformers; development of an expert system enabling diagnostics of a power transformer during its normal operation using the acoustic emission method; research and analysis of the impact of noise and infrasound generated by power infrastructure on living organisms.
Special Issues, Collections and Topics in MDPI journals
Dr. Marcin Bernaś

E-Mail Website
Guest Editor
Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland
Interests: road traffic; soft computing; data collection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tomasz Boczar

E-Mail Website
Guest Editor
Institute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
Interests: issues related to electrical engineering; power engineering; renewable energy sources; automatic diagnostic methods of insulation systems of power equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent manufacturing and industrial engineering are undergoing rapid transformation with the emergence of new technologies. This Special Issue explores recent advancements and applications in this area, focusing on integrating cutting-edge technologies to enhance efficiency, productivity, and sustainability.

We invite high-quality scientific papers that present original research and reviews on various aspects of intelligent manufacturing and industrial engineering. Contributions covering a wide range of topics within the journal's scope are encouraged, emphasizing the latest developments in the field.

We welcome submissions focusing on, but not limited to, the following topics:

  1. Artificial intelligence and machine learning applications in manufacturing.
  2. Robotics and automation in industrial processes.
  3. Additive manufacturing (3D printing) technologies and applications.
  4.  Cyber-physical systems and smart manufacturing.
  5. Internet of Things (IoT) for industrial applications.
  6. Advanced materials and nanotechnology in manufacturing.
  7. Sustainable manufacturing practices and green technologies.
  8. Digital twins and virtual manufacturing environments.
  9. Human factors and ergonomics in industrial engineering.
  10. Supply chain optimization and logistics management.
  11. Quality control and Six Sigma methodologies.

We encourage researchers and experts from diverse backgrounds to contribute their expertise and insights to this Special Issue. We look forward to receiving your submissions.

Dr. Daniel Jancarczyk
Dr. Marcin Bernaś
Prof. Dr. Tomasz Boczar
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

  • intelligent manufacturing
  • industrial engineering
  • new technologies
  • advancements and applications
  • artificial intelligence
  • machine learning
  • robotics
  • automation
  • additive manufacturing
  • cyber-physical systems
  • Internet of Things
  • sustainable manufacturing
  • digital twins
  • supply chain optimization
  • quality control

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 1648 KB  
Article
Application of Recurrent Neural Networks for Time-Series Analysis of Low-Frequency Signals Generated by Power Transformers
by Daniel Jancarczyk, Marcin Bernas and Tomasz Boczar
Appl. Sci. 2026, 16(9), 4295; https://doi.org/10.3390/app16094295 - 28 Apr 2026
Abstract
Traditional diagnostics of power transformers heavily rely on signal transformations, such as Welch’s method, to analyze low-frequency noise signals. This study proposes a novel approach using Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for direct time-series analysis of raw low-frequency [...] Read more.
Traditional diagnostics of power transformers heavily rely on signal transformations, such as Welch’s method, to analyze low-frequency noise signals. This study proposes a novel approach using Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for direct time-series analysis of raw low-frequency signals without frequency-domain transformation. By training and testing multiple LSTM architectures on transformer vibroacoustic data, the proposed approach achieved approximately 86% accuracy in the fine-grained multi-class benchmark and up to 95.54% in the broader grouped categorization scenario. The model further demonstrated near-perfect classification accuracy in distinguishing transformer types (normal vs. overload) using a simplified RNN architecture. These findings illustrate that RNN-based models can streamline transformer diagnostics and improve accuracy in identifying operational states and types, potentially advancing non-invasive monitoring techniques in power system infrastructure. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering, 2nd Edition)
22 pages, 6191 KB  
Article
Estimations of Production Capacity Based on Simulation Models: A Case Study of Furniture Manufacturing Systems
by Damian Kolny and Robert Drobina
Appl. Sci. 2026, 16(4), 1683; https://doi.org/10.3390/app16041683 - 7 Feb 2026
Viewed by 435
Abstract
This article presents the concept of building a discrete event simulation model of a production system in terms of statistical and probabilistic models, which is based on a fragment of a broader production process in the furniture industry. The purpose of the study [...] Read more.
This article presents the concept of building a discrete event simulation model of a production system in terms of statistical and probabilistic models, which is based on a fragment of a broader production process in the furniture industry. The purpose of the study was to evaluate the efficiency of a single-shift production process during the start-up phase and to determine the impact of implementing two- and three-shift systems. The discrete event simulation model was developed using actual production data collected during a single-shift operation. Scenarios were then designed to identify and quantify the necessary process adjustments required for the successful implementation of two- and three-shift systems. The authors demonstrated that simulation modeling of production processes based on probabilistic distributions provides information that is essential for effective capacity planning. The proposed percentile grids enabled clear visualization and precise assessment of production resource utilization in various shift configurations, facilitating decision-making regarding capacity expansion based on previously assumed data. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering, 2nd Edition)
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