Development of Robotic and Hybrid Manufacturing Systems for Machining in Smart Factories

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1015

Special Issue Editors


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Guest Editor
Deseño na enxeñaría, Universidad de Vigo, Sede Campus, 36310 Vigo, Spain
Interests: industrial processes; mechanical engineering and technology; maintenance engineering; industrial machinery and equipment

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Guest Editor
Deseño na Enxeñaría, Universidad de Vigo, Sede Campus, 36310 Vigo, Spain
Interests: robotic machining

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Guest Editor
Department of Mechanical Engineering, ISEP–School of Engineering, Polytechnic of Porto, 4200-465 Porto, Portugal
Interests: lean manufacturing; manufacturing systems; discrete event simulation; simulation and optimization
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Special Issue Information

Dear Colleagues,

Robotic solutions are profoundly transforming the design of industrial hybrid production systems. The ability to connect the physical and digital worlds through Industry 4.0 is enabling the development and optimization of manufacturing processes, fostering interest in the adoption of new technologies. However, robots that handle material removal processes are still struggling to achieve narrow and repeatable manufacturing tolerances, especially for hard materials.

The strong interest from manufacturing industries in the development and implementation of robotic and hybrid machining systems, due to their cost-saving potential, has prompted one of the most significant trends in technological research to advance this field of knowledge. This Special Edition aims to gather innovative contributions on this topic from perspectives focusing on the design and development of novel solutions that represent a step forward in the integration of systems in smart factories. Specifically, contributions can address aspects of "Design and development of methods, methodologies, or techniques for...":

  • Trends and challenges in the development of robotic and hybrid machining solutions.
  • Intelligent, intuitive, and robust control to estimate or compensate for trajectory deviations during the additive manufacturing and machining process.
  • Methodologies, methods, or techniques for the design of robotic hybrid manufacturing systems.
  • Integration of Industry 4.0 technologies to introduce improvements in the implementation of robotic manufacturing process.
  • Human-machine interfaces for user interaction and training.

This Special Edition will include both review articles and in-depth research on new advancements in this field.

Prof. Dr. José Enrique Ares Gómez
Dr. Iván Iglesias Sánchez
Dr. Luís Pinto Ferreira
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. Machines is an international peer-reviewed open access monthly 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.

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Published Papers (1 paper)

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Research

25 pages, 14457 KiB  
Article
On the Optimization of Robot Machining: A Simulation-Based Process Planning Approach
by Thanassis Souflas, Christos Gerontas, Harry Bikas and Panagiotis Stavropoulos
Machines 2024, 12(8), 521; https://doi.org/10.3390/machines12080521 - 31 Jul 2024
Viewed by 351
Abstract
The use of industrial robots for machining operations is pursued by industry lately, since they can increase the flexibility of the production system and reduce production costs. However, their industrial adoption is still limited, mainly due to their insufficient structural stiffness and posture-dependent [...] Read more.
The use of industrial robots for machining operations is pursued by industry lately, since they can increase the flexibility of the production system and reduce production costs. However, their industrial adoption is still limited, mainly due to their insufficient structural stiffness and posture-dependent dynamic behavior, leading to limited machining process accuracy. For this purpose, the Digital-Model of a machining robot has been developed, providing a tool for virtual commissioning of the process that can be used during the process planning stage. The Multi-Body Simulation method combined with a Component Mode Synthesis have been adopted, considering flexibility of both the joints and links. On top of that, and motivated from robotic-based machining systems’ flexibility and versatility, two optimization algorithms have been developed, attempting to increase the process accuracy. A workpiece placement optimization algorithm, attempting to maximize the robot stiffness during the process acquiring knowledge from the robot stiffness maps, and a feed-rate scheduling algorithm, attempting to constrain the contour error by regulating the generated cutting forces. The capabilities and functionality of the developed model and optimization algorithms are showcased in two different case studies, with the results proving the improvements on the process accuracy after the application of the optimization algorithms. Finally, an experimental validation of the Digital-Model has been performed, to confirm the consistency between model outputs and real experimental data. Full article
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