Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Smart Manufacturing and Autonomous Systems for Sustainable Development
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Manufacturing and Autonomous Systems for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2666

Special Issue Editors

Dr. Jian Wan

E-Mail Website
Guest Editor
School of Engineering and Technology, Aston University, Birmingham B4 7ET, UK
Interests: robotics and autonomous systems; artificial intelligence and machine learning; control systems engineering; constrained optimistion; estimation and control
Prof. Dr. Yuchun Xu

E-Mail Website
Guest Editor
School of Engineering and Technology, Aston University, Birmingham B4 7ET, UK
Interests: smart and sustainable manufacturing; life cycle engineering and optimisation; digital product development and manufacturing; cost modelling & engineering economic analysis; circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Zhang

E-Mail Website
Guest Editor
College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
Interests: artificial intelligent for condition monitoring; fault diagnosis; prognostic health management; especially for deep learning; transfer learning; few-shot learning method and their application for the large industrial environment; reinforcement learning for control and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technologies play a vital role in meeting the 2030 Agenda for Sustainable Development. Among these technologies, smart manufacturing and autonomous systems, as well as other emerging digital technologies, are being put into practice to improve energy efficiency, reduce carbon emissions, create a circular economy and conduct environmental conservation, in addition to other activities. Their impact on sustainability is yet to be widely publicized, which is important for the promotion of further research in this field.

The aim of this Special Issue is to publish the latest developments in the application of digital technologies and autonomous systems to address sustainable development issues such as energy efficiency, circular economy, and environmental conservation. Some related fields include, but are not limited to, the following:

  • Smart manufacturing for energy efficiency;
  • Remanufacturing for circular economy;
  • Robotics and autonomous systems for environmental conservation;
  • Artificial intelligence and machine learning for situation awareness;
  • Industry 4.0;
  • Digital technologies for sustainability.

We look forward to receiving your contributions.

Dr. Jian Wan
Prof. Dr. Yuchun Xu
Dr. Ming Zhang
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. Sustainability 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

  • sustainability
  • smart manufacturing
  • autonomous systems
  • machine learning

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 4667 KiB  
Article
Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset
by Jihong Yan, Mingyang Zhang and Yuchun Xu
Sustainability 2023, 15(20), 15051; https://doi.org/10.3390/su152015051 - 19 Oct 2023
Viewed by 872
Abstract
The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding [...] Read more.
The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability. Full article
(This article belongs to the Special Issue Smart Manufacturing and Autonomous Systems for Sustainable Development)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 3792 KiB  
Review
Automated Monitoring of the Uniform Demagnetization Faults in Permanent-Magnet Synchronous Motors: Practical Methods and Challenges
by Junxiang Li, Ziang Li, Jian Zhang, Shuyuan Zhao, Feitian Cheng, Chuan Qian, Xingyu Hu and Guoxiang Zhou
Sustainability 2023, 15(23), 16326; https://doi.org/10.3390/su152316326 - 27 Nov 2023
Viewed by 1059
Abstract
Due to its high power, high efficiency, low pollution, and compact size, permanent-magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices, which are used in renewable, sustainable, and environmentally friendly [...] Read more.
Due to its high power, high efficiency, low pollution, and compact size, permanent-magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices, which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high-temperature and high-current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor’s permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detection methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives are summarized and their benefits and challenges are reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article not only sheds light on the origins of the automated monitoring of demagnetization faults but also helps to design highly effective and sustainable permanent-magnet synchronous motors. Full article
(This article belongs to the Special Issue Smart Manufacturing and Autonomous Systems for Sustainable Development)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop