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Machines
Special Issues
Smart Manufacturing Systems Towards Sustainability and Zero-Defect Manufacturing
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Smart Manufacturing Systems Towards Sustainability and Zero-Defect Manufacturing

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 5274

Special Issue Editors

Dr. Bugra Alkan

E-Mail Website
Guest Editor
School of Engineering, London South Bank University, 103 Borough Rd., London SE1 0AA, UK
Interests: smart manufacturing; IoT; robotics; manufacturing systems; industry 4.0; operations research; optimization; assembly systems
Special Issues, Collections and Topics in MDPI journals
Dr. Manoj Babu

E-Mail Website
Guest Editor
WMG, University of Warwick, Coventry (CV4 7AL), UK
Interests: applied machine learning; digital manufacturing; spatio-temporal data analysis; part shape error modelling
Dr. Malarvizhi Kaniappan Chinnathai

E-Mail Website
Guest Editor
Brunel Innovation Centre, Brunel University, Cambridge, UK
Interests: zero defect manufacturing; applied artificial intelligence; convolutional neural networks; industrial automation and robotics; simulation-based optimisation; industry 4.0, digital twin
Dr. Tianzhu Sun

E-Mail Website
Guest Editor
Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK
Interests: laser welding; friction stir welding; materials processing; industry 4.0; process monitoring; digital manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent targets on carbon emission reductions have seen manufacturers and researchers around the globe explore and implement various technologies and solutions to i) reduce their direct energy consumption and wastage, and ii) reduce the indirect energy lost as scrap and rework. In order to achieve these targets, it is important that manufacturing industries gear towards Sustainable Smart Manufacturing, wherein Industry 4.0 technologies such as Artificial Intelligence, Digital Twins, and Big Data Analytics are harnessed to achieve sustainability.

This Special Issue aims to address the challenges and opportunities in utilising these Industry 4.0 technologies to enable Sustainable Smart Manufacturing. Novel ideas associated with waste reduction, energy consumption monitoring, defect prevention and detection, and improving material efficiency coupled with advancements in digitalisation, big data analytics and Internet of Things can help realise the transition towards a smarter, cleaner production system. Embedding intelligence into digital models enables the monitoring of equipment and schedule their maintenance, detect wear and tear, and even allows making autonomous decisions. This Special Issue will be a synthesis of innovative strategies, trends, and state-of-the art technologies in the domain of condition monitoring, zero-defect manufacturing, artificial intelligence, and digital manufacturing that drive the transformation towards sustainable manufacturing.   

Topics of interest include, but are not limited to:

  • Sustainable Industry 4.0;
  • Energy monitoring and energy big data;
  • Data analytics and data-driven approaches for sustainable manufacturing;
  • Digital twin and simulation for energy analysis;
  • Zero-defect manufacturing;
  • Right first-time approach;
  • Machine learning for defect detection;
  • Preventive maintenance and condition monitoring;
  • Big data analytics, process mining and data mining;
  • Applied artificial intelligence and expert systems;
  • Cloud manufacturing and cybersecurity in smart manufacturing;
  • Green manufacturing (waste management, resources saving, energy efficiency);
  • Circular economy and its implications on manufacturing;
  • Sustainable materials;
  • Eco-design of smart manufacturing systems; and
  • Advanced material and lightweight manufacturing.

Dr. Bugra Alkan
Dr. Manoj Babu
Dr. Malarvizhi Kaniappan Chinnathai
Dr. Tianzhu Sun
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.

Keywords

  • smart manufacturing
  • Industry 4.0
  • sustainable manufacturing
  • zero defect manufacturing
  • digital manufacturing
  • cyber-physical systems
  • energy efficiency
  • applied machine learning
  • big data analytics
  • smart factory
  • predictive maintenance
  • green manufacturing

Published Papers (2 papers)

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Research

22 pages, 14922 KiB  
Article
Virtual Engineering and Commissioning to Support the Lifecycle of a Manufacturing Assembly System
by Sergey Konstantinov, Fadi Assad, Bilal Ahmad, Daniel A. Vera and Robert Harrison
Machines 2022, 10(10), 939; https://doi.org/10.3390/machines10100939 - 16 Oct 2022
Cited by 8 | Viewed by 2213
Abstract
Prior to the physical build of the industrial automation system, some challenges arise, such as processes’ cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component [...] Read more.
Prior to the physical build of the industrial automation system, some challenges arise, such as processes’ cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component parameters. As a result, the lifecycle of the system development is prolonged, and the potential for erroneous performance increases. In modern digital manufacturing environments, virtual engineering (VE) and virtual commissioning (VC) serve as effective tools to tackle the aforementioned problems and their consequences. The virtual models developed for VE and VC not only assist system developers in the physical build stage but also in the following stages of the system lifecycle by providing a common virtual model, a digital twin (DT), of the manufacturing processes and the product. This developed model should possess the ability to simulate the system behaviour, e.g., the mechanics, kinematics, speed and acceleration profiles. Three stakeholders are involved in the development process: the machine builder, system integrator and end user. The current work focuses on the virtual engineering approach to support the entire lifecycle of a manufacturing system from the machine builder, system integrator and end user perspectives. For this purpose, it puts forward a systematic methodology of implementing VC and VE using a toolset developed by the Automation Systems Group at the University of Warwick within an industrial project. The suggested methodology is illustrated in a case study where a digital twin of a physical station was modelled, developed and tested in parallel with the physical machine development and build. Finally, the benefits and limitations are highlighted based on the gained outcomes and the implemented activities. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems Towards Sustainability and Zero-Defect Manufacturing)
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16 pages, 6266 KiB  
Article
Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images
by Sulochana Subramaniam, Jamil Kanfoud and Tat-Hean Gan
Machines 2022, 10(10), 839; https://doi.org/10.3390/machines10100839 - 21 Sep 2022
Cited by 5 | Viewed by 2046
Abstract
Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique [...] Read more.
Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems Towards Sustainability and Zero-Defect Manufacturing)
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