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Robotics in Manufacturing Processes
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Robotics in Manufacturing Processes

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1535

Special Issue Editors

Prof. Dr. George-Christopher Vosniakos

E-Mail Website
Guest Editor
School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: robotics in manufacturing; optimal process planning; VR in manufacturing; intelligence in Industry 4.0; 3D printer design and control; CNC machine tools
Special Issues, Collections and Topics in MDPI journals
Dr. Panorios Benardos

E-Mail Website
Guest Editor
School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: modelling and optimization of manufacturing processes and systems; industrial robotics in manufacturing; machine learning; machine vision; industrial internet of things (IIoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial robots play an important role in various manufacturing processes, often replacing machine tools. Indicative applications related to the scope of this SI concern the following:

  • Welding (e.g., arc, friction stir, spot, etc.).
  • Painting and coating.
  • Machining.
  • Additive manufacturing (e.g., WAAM for large parts).
  • Textile composite layup.
  • Assembly (both mechanical and mechatronic parts).
  • Inspection (manufacturing defect identification, dimension measurement).

Current issues are expected to be addressed in this SI in relation to the application of robots in manufacturing processes, the following being indicative:

  • Advancements in sensors, vision systems, actuators, grippers, and robot structures.
  • Advanced control algorithms, e.g., real-time adaptive strategies for enhancing manufacturing process precision and efficiency.
  • Machine learning and artificial intelligence in robot control.
  • Novel fast and/or intuitive programming of robots for manufacturing operations.
  • Human–robot interaction and collaboration in performing manufacturing tasks.
  • Integration of industrial robots into cyber–physical systems and Industry 4.0 via the Internet of things, digital twins, and smart technologies with specific reference to manufacturing processes fostered in specific industrial sectors.
  • Microrobots in micro-manufacturing.
  • Impact of robotics on sustainable and eco-friendly manufacturing practices.

Please note that material-handling applications, such as picking, placing, packaging, palletizing, and machine tending, are outside the scope of the SI if they are not shown to be closely related to a manufacturing process.

Prof. Dr. George-Christopher Vosniakos
Dr. Panorios Benardos
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. Journal of Manufacturing and Materials Processing 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 1800 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

  • industrial robotics
  • manufacturing process
  • vision
  • sensors
  • digital twins
  • human–robot collaboration
  • intuitive programming
  • machine learning

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Published Papers (2 papers)

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Research

17 pages, 4120 KiB  
Article
Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts
by Eber L. Gouveia, John G. Lyons and Declan M. Devine
J. Manuf. Mater. Process. 2024, 8(5), 218; https://doi.org/10.3390/jmmp8050218 - 30 Sep 2024
Viewed by 393
Abstract
The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a [...] Read more.
The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments. Full article
(This article belongs to the Special Issue Robotics in Manufacturing Processes)
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40 pages, 19638 KiB  
Article
A Statistical Analysis of Commercial Articulated Industrial Robots and Cobots
by Peyman Amiri, Marcus Müller, Matthew Southgate, Theodoros Theodoridis, Guowu Wei, Mike Richards-Brown and William Holderbaum
J. Manuf. Mater. Process. 2024, 8(5), 216; https://doi.org/10.3390/jmmp8050216 - 30 Sep 2024
Viewed by 757
Abstract
This paper aims to elucidate the state-of-the-art, prevailing priorities, and the focus of the industry, and identify both limitations and potential gaps regarding industrial robots and collaborative robots (cobots). Additionally, it outlines the advantages and disadvantages of cobots compared to traditional industrial robots. [...] Read more.
This paper aims to elucidate the state-of-the-art, prevailing priorities, and the focus of the industry, and identify both limitations and potential gaps regarding industrial robots and collaborative robots (cobots). Additionally, it outlines the advantages and disadvantages of cobots compared to traditional industrial robots. Furthermore, three novel factors are introduced in this survey as metrics to evaluate the efficiency and performance of industrial robots and cobots. To achieve these purposes, a statistical analysis and review of commercial articulated industrial robots and cobots are conducted based on their documented specifications, such as maximum payload, weight, reach, repeatability, average maximum angular speed, and degrees of freedom (DOF). Additionally, the statistical distributions of the efficiency factors are investigated to develop a systematic method for robot selection. Finally, specifications exhibiting strong correlations are compared in pairs using regressions to find out trends and relations between them, within each company and across them all. The investigation of the distribution of specifications demonstrates that the focus of the industry and robot makers is mostly on articulated industrial robots and cobots with higher reach, lower payload capacity, lower weight, better repeatability, lower angular speed, and six degrees of freedom. The regressions reveal that the weight of robots increases exponentially as the reach increases, primarily due to the added weight and torque resulting from the extended reach. They also indicate that the angular speed of robots linearly decreases with increasing reach, as robot manufacturers intentionally reduce the angular speed through reductive gearboxes to compensate for the additional torque required as the reach extends. The trends obtained from the regressions explain the reasons behind these interrelationships, the design purpose of robot makers, and the limitations of industrial robots and cobots. Additionally, they help industries predict the dependent specifications of articulated robots based on the specifications they require. Moreover, an accompanying program has been developed and uploaded on to GitHub, taking the required specifications and returning a list of proper and efficient robots sourced from different companies according to the aforementioned selection method. Full article
(This article belongs to the Special Issue Robotics in Manufacturing Processes)
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