Special Issue "Manufacturing Energy Efficiency and Industry 4.0"
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Sustainable Energy".
Deadline for manuscript submissions: 31 October 2021.
Special Issue Editors
Interests: sustainable manufacturing systems; green manufacturing; environmental impact assessment; simulation; modelling
Special Issues and Collections in MDPI journals
Interests: industrial sustainability; simulation and modeling; sensor technologies; systems engineering, throughlife engineering services; instrumentation and sensors; Industry 4.0
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
Energy efficiency in manufacturing systems and processes will carry on being a key research topic. Research in the energy efficiency of manufacturing in the last decade has resulted in very promising improvements. While design-time energy efficiency considerations have received considerable attention, operating time efficiency is now increasingly benefitting from the adoption and implementation of Industry-4.0-enabling technologies. The Industrial Internet of Things (IIoT), the upgrade of manufacturing facilities into industrial cyber physical systems (ICPS), and the efficient exploitation of manufacturing and process monitoring data through advanced Machine Learning present concrete opportunities towards more responsive, smarter, and more energy-efficient manufacturing.
This Special Issue considers the energy efficiency of both manufacturing processes and systems and how these can be improved by the use of Industry-4.0-enabling technologies. Papers are particularly invited in the following areas:
- Industry-4.0-enabled methods for the real time measurement of energy efficiency;
- Machine learning from production line monitoring data for energy efficiency;
- Tools and techniques in the context of Industry 4.0 for the analysis and development of improvements with regards to energy consumption;
- Tools and techniques for the modeling and simulation of energy efficiency;
- Case studies on the management of Industry 4.0 systems for energy efficiency;
- Industry 4.0 and decarbonization of manufacturing;
- Enabling technologies for green, lean, and smart manufacturing;
- Joint asset and production management aided by Industry 4.0 technologies.
Prof. Dr. Konstantinos Salonitis
Dr. Christos Emmanouilidis
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 papers will be 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. Energies 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 2000 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
- Green manufacturing
- Industrial Internet of Things (IIoT)
- Industry 4.0
- Industrial cyberphysical systems (ICPS)
- Big data management in manufacturing
- Industrial sustainability
- Artificial Intelligence and machine learning for energy-efficient manufacturing
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Process Planning Framework for Sustainablemanufacturing
Authors: Colin Reiff; Matthias Buser; Thomas Betten
Affiliation: 1 Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Stuttgart, Baden-Württemberg, Germany
2 Institut für Strahlwerkzeuge (IFSW), University of Stuttgart, Stuttgart, Baden-Württemberg, Germany
3 Institute of Acoustics and Building Physics (IABP), University of Stuttgart, Stuttgart, Baden-Württemberg, Germany
Abstract: The increasing importance of environmental protection affects not only our daily lives, but also the manufacturing industry in particular. Resource-saving production is of enormous importance in order to remain competitive, and also to comply with the increasingly stringent legislation, and customer demands.
At present, environmental factors play a subordinate role for the choice of manufacturing processes during process planning for highly-customized products. As a consequence, opportunities are not taken into account to reduce environmental impacts, which could also save costs and thus improve the economic efficiency of the production processes.
In order to support manufacturing experts during process planning, this paper presents an approach of a highly-automated selection process of manufacturing resources with regard to environmental aspects for individual parts. This is achieved by abstracting and generalizing manufacturing resources and part descriptions to dynamically calculate the resulting price, required time, and environmental impacts for the manufacturing of a specific part. The approach is implemented using a web framework. The main advantage is the possibility to model and compare different manufacturing processes within a single framework, which is demonstrated by exemplary use cases.