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Machines
Special Issues
Recent Developments in Machine Design, Automation and Robotics
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Recent Developments in Machine Design, Automation and Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 899

Special Issue Editor

Dr. Raul D. S. G. Campilho

E-Mail Website
Guest Editor
Departamento de Engenharia Mecânica, Instituto Superior de Engenharia do Porto, 4200-072 Porto, Portugal
Interests: advanced manufacturing systems; automation and robotics; industrial design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The competitiveness of companies in the global market highly depends on the efficiency of industrial processes, which rely on technologically advanced machines and equipment. Moreover, through the extensive use of automation and robotics, it is possible to attain the required product quality, production flexibility to adapt to new product references, production rate, and low fabrication costs. Throughout time, automation and robotics became the best way to achieve the goals of the market. Therefore, these technologies are subject to continuous evolution, constantly presenting new solutions. Major advances and developments were recently experienced, both academically and industrially, with emphasis on the following:

  • Collaborative robotics (cobots): human–robot collaboration in industrial settings, safety protocols and advancements in cobot technology, and cobots in small and medium-sized enterprises.
  • Advanced control systems in automation: adaptive and predictive control algorithms, real-time control strategies for industrial processes, and integration of AI and machine learning in control systems.
  • Additive manufacturing for machine design: 3D printing applications in machine parts and design, and optimization and material advancements in additive manufacturing.
  • Smart factory and Industry 4.0: Internet of Things (IoT) applications in manufacturing, cyber–physical systems and their role in modern factories, and digital twins for predictive maintenance and optimization.
  • Sustainable manufacturing and green design: energy-efficient design and automation, recycling and eco-friendly materials in machine design, and sustainable practices in industrial robotics and automation.
  • Machine learning in robotics: reinforcement learning for robotic applications, vision-based learning and object recognition in robotics, and autonomous decision-making in robotic systems.

This Special Issue intends to bring together a significant number of contributions in this area through the publication of high-quality original works in the field, subsequently promoting its dissemination through the Open Access system.

Dr. Raul D. S. G. Campilho
Guest Editor

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.

Keywords

  • machine design
  • industrial automation
  • industrial robotics
  • collaborative robotics
  • advanced control systems
  • additive manufacturing
  • smart factory
  • Industry 4.0
  • Internet of Things (IoT)
  • sustainable manufacturing
  • green design
  • machine learning in robotics
  • automation technologies
  • robotics integration
  • adaptive control systems

Published Papers (1 paper)

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Research

15 pages, 1589 KiB  
Article
AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study
by Davide Fabiocchi, Nicola Giulietti, Marco Carnevale and Hermes Giberti
Machines 2024, 12(4), 257; https://doi.org/10.3390/machines12040257 - 12 Apr 2024
Viewed by 462
Abstract
The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is [...] Read more.
The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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