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Smart Production in Terms of Industry 4.0 and 5.0
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Department of Engineering Processes Automation and Integrated Manufacturing Systems, Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A Str., 44-100 Gliwice, Poland
Dr. Cezary Grabowik
Department of Engineering Processes Automation and Integrated Manufacturing Systems, Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland
Faculty of Manufacturing Technologies, Technical University of Košice, 080 01 Prešov, Slovakia
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Smart Production in Terms of Industry 4.0 and 5.0

Abstract submission deadline
30 June 2024
Manuscript submission deadline
31 October 2024
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Topic Information

Dear Colleagues,

This Topic is dedicated to the fourth and fifth industrial revolutions and aims to promote new intelligent technologies, artificial intelligence and machine learning methods in the fields of materials, manufacturing, enterprise/factory and even maintenance to make them smart. New mechatronic concepts of Cyber Physical Systems (CPSs) connected in the dimension of the Internet of Things (IoT) for production lines, machines, operators, robots, tools and other equipment are promoted. One of the dimensions of the fourth industrial revolution is the advanced combination of virtual and real tools and tests to improve the efficiency of modern engineering science. Standards for integration, communication and interaction between CPSs of robots, humans and machines are strongly needed to bridge the gap between theory and practice. Network connections between CPSs and physical systems are characterized by a new level of awareness of their process capabilities, including various objectives such as production lead times, production on time, customized products, higher production control, increased product quality, equipment reliability and availability, and more. Papers presenting new manufacturing paradigms, including smart connections, data-to-information conversion, cyber, cognition and configuration levels are welcome. Other aspects of Industry 4.0 are Big Data processing, cloud computing, blockchain technology, etc. Models of sensor data integration with CPS should be developed, with the need to periodically query the controlled sensor network and process the received data in real time. Moreover, analysis based on historical data must be synchronized with real-time conditions of machines, robots, devices and workers to enhance the capabilities of the physical and virtual world. For each domain of a manufacturing process, models of edge computing and knowledge acquisition are needed at a specific moment and for a censored time interval.

For this Topic, we invite you to submit original papers and reviews of the struggles of scientists and practitioners in the fields of Industry 4.0 and 5.0. We encourage you to publish studies containing the results of conceptual work and laboratory and real-object tests to present issues including the use of smart connections, data-to-information conversion, cyber, cognition and configuration levels and more.

Dr. Iwona Paprocka
Dr. Cezary Grabowik
Dr. Jozef Husar
Topic Editors

Keywords

  • reverse engineering
  • additive manufacturing
  • digital twins
  • human–robot collaboration
  • predictive maintenance
  • virtual, augmented and mixed reality in production
  • production process automation and simulation
  • machine learning
  • production planning and scheduling
  • artificial intelligence in production and material processing
  • smart materials and their application

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.7 4.5 2011 16.9 Days CHF 2400 Submit
Electronics
electronics 		2.9 4.7 2012 15.6 Days CHF 2400 Submit
IoT
IoT 		- 5.2 2020 23.3 Days CHF 1200 Submit
Materials
materials 		3.4 5.2 2008 13.9 Days CHF 2600 Submit
Robotics
robotics 		3.7 5.9 2012 17.3 Days CHF 1800 Submit
Sensors
sensors 		3.9 6.8 2001 17 Days CHF 2600 Submit
Machines
machines 		2.6 2.1 2013 15.6 Days CHF 2400 Submit

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Published Papers (1 paper)

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21 pages, 12038 KiB  
Technical Note
Image-to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems
by Timotei István Erdei, Tibor Péter Kapusi, András Hajdu and Géza Husi
Robotics 2024, 13(6), 88; https://doi.org/10.3390/robotics13060088 (registering DOI) - 2 Jun 2024
Abstract
Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition [...] Read more.
Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset. Full article
(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)
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