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Applied Sciences
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New Technologies in Intelligent Manufacturing and Industrial Engineering
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New Technologies in Intelligent Manufacturing and Industrial Engineering

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

Deadline for manuscript submissions: 30 March 2025 | Viewed by 5578

Special Issue Editors

Dr. Daniel Jancarczyk

E-Mail Website
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
Dr. Marcin Bernaś

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Computer Science, Department of Computer Science and Automation, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland
Interests: road traffic; soft computing; data collection
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,

The field of intelligent manufacturing and industrial engineering is undergoing rapid transformation with the emergence of new technologies. This Special Issue aims to explore recent advancements and applications in this area, focusing on the integration of 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 scope of the journal are encouraged, with an emphasis on 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 100 words) can be sent to the Editorial Office for announcement on this website.

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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

25 pages, 21379 KiB  
Article
Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
by Tomaž Pušnik and Aleš Hace
Appl. Sci. 2024, 14(21), 9808; https://doi.org/10.3390/app14219808 - 27 Oct 2024
Viewed by 704
Abstract
Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the [...] Read more.
Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the diverse geometrical characteristics of workpieces that could potentially allow for different machining speeds. Our study introduces a region-based approach, which improves surface finishing machining time by allowing variable speeds and directions tailored to each region’s specific characteristics. This method leverages a task-oriented strategy integrating robot kinematics and workpiece surface geometry, subdivided by the clustering algorithm. Subsequently, methods for optimization algorithms were developed to calculate each region’s optimal machining speeds and directions. The efficacy of this approach was validated through numerical results on two distinct workpieces, demonstrating significant improvements in machining times. The region-based approach yielded up to a 37% reduction in machining time compared to traditional single-direction machining. Further enhancements were achieved by optimizing the workpiece positioning, which, in our case, added up to an additional 16% improvement from the initial position. Validation processes were conducted to ensure the collaborative robot’s joint velocities remained within safe operational limits while executing the region-based surface finishing strategy. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering)
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18 pages, 4318 KiB  
Article
Intelligent Framework Design for Quality Control in Industry 4.0
by Yousaf Ali, Syed Waqar Shah, Arsalan Arif, Mehdi Tlija and Mudasir Raza Siddiqi
Appl. Sci. 2024, 14(17), 7726; https://doi.org/10.3390/app14177726 - 2 Sep 2024
Viewed by 1324
Abstract
This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of [...] Read more.
This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering)
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20 pages, 973 KiB  
Article
Computer-Aided Design and Additive Manufacturing for Automotive Prototypes: A Review
by Marcos Vido, Geraldo Cardoso de Oliveira Neto, Sergio Ricardo Lourenço, Marlene Amorim and Mário Jorge Ferreira Rodrigues
Appl. Sci. 2024, 14(16), 7155; https://doi.org/10.3390/app14167155 - 15 Aug 2024
Cited by 1 | Viewed by 2910
Abstract
This study investigated the integration of computer-aided design (CAD) and additive manufacturing (AM) in prototype production, particularly in the automotive industry. It explores how these technologies redefine prototyping practices, with a focus on design flexibility, material efficiency, and production speed. Adopting the Preferred [...] Read more.
This study investigated the integration of computer-aided design (CAD) and additive manufacturing (AM) in prototype production, particularly in the automotive industry. It explores how these technologies redefine prototyping practices, with a focus on design flexibility, material efficiency, and production speed. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, this study encompasses a systematic review of 28 scholarly articles. It undertakes a comprehensive analysis to identify key themes, trends, and gaps in the existing research on CAD and AM integration in automotive prototyping. This study revealed the significant advantages of CAD and AM in prototype manufacturing, including improved design capabilities, efficient material usage, and the creation of complex geometries. It also addresses ongoing challenges, such as technology integration costs, scalability, and sustainability. Furthermore, this study foresees future developments by focusing on enhancing CAD and AM technologies to meet evolving market demands and optimize performance. This study makes a unique contribution to the literature by providing a detailed overview of the integration of CAD and AM in the context of automotive prototyping. This study incorporates valuable insights into the current practices and challenges and future prospects, potentially leading to more advanced, sustainable, and customer-oriented prototyping methods in the automotive sector. Full article
(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering)
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