Special Issue "Maintenance 4.0 Technologies for Sustainable Manufacturing"
Deadline for manuscript submissions: 31 October 2021.
Interests: manufacturing engineering; maintenance technologies; maintenance management; multi-criteria decision making methods; sustainability performance
Manufacturing companies and equipment manufacturers face two major trends affecting their business: digitalization and sustainability. The literature on production refers to digitalization as one of the pillars of the next (fourth) Industrial Revolution. In the Industry 4.0 era, manufacturing systems are able to monitor physical processes and make smart decisions through real-time communication and cooperation with humans, machines, sensors, etc. The second major trend affecting manufacturers is sustainability. In the sustainable development environment, there has been an increased pressure on manufacturing companies to think beyond traditional economic measures and evaluate environmental and social effects of the business.
In this context, various initiatives and approaches are set up to help companies adopt the principles of the fourth Industrial Revolution with respect to sustainability. Within these actions, the use of contemporary maintenance approaches such as Maintenance 4.0 is highlighted as one of the prevailing sustainable manufacturing topics. Minimized downtime, prolonged machine life, increased production efficiencies, resource utilization, and reduced costs are merely a few promising prospects of Maintenance 4.0 technologies.
The objective of this Special Issue is to present the latest advances and developments of new methods, techniques, systems, and tools dedicated to the application of Maintenance 4.0 technologies for economic, environmental, and social challenges of sustainable manufacturing.
Topics and themes can include but are not limited to:
- Drivers and barriers for the implementation of Maintenance 4.0 technologies in manufacturing companies;
- Intelligent decision support for sustainable maintenance practices;
- Human factors, industrial ergonomics, and safety in smart maintenance;
- Modeling and simulation of smart maintenance systems;
- Big Data analytics implementation for sustainable maintenance;
- Digital-twin-driven intelligent maintenance for sustainability;
- Internet of Things solutions in maintenance for sustainability;
- Data-driven maintenance and product lifecycle management systems;
- Causes and effects of implementing Maintenance 4.0 technologies for sustainable manufacturing.
Dr. Jasiulewicz-Kaczmarek Małgorzata
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. Applied Sciences 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.
- Predictive maintenance
- Prescriptive maintenance
- Big Data analytics
- Digital twin
- Internet of things
- Augmented/virtual reality
- 3D printing
- Remaining useful life
- Resource efficiency