Special Issue "Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 31 August 2022 | Viewed by 4635

Special Issue Editors

Dr. Leonilde Varela
E-Mail Website
Guest Editor
Department of Production and Systems, School of Engineering, University of Minho, Braga, Portugal
Interests: manufacturing management; collaborative networks and platforms; decision-support models and systems; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Goran D. Putnik
E-Mail Website1 Website2
Guest Editor
University of Minho, Braga, Portugal
Interests: integrated; distributed; agile; and virtual manufacturing systems and enterprises; cloud and ubiquitous manufacturing; learning organizations; Industry 4.0

Special Issue Information

Dear Colleagues,

Collaborative networks and systems (CNS) have received much attention in recent decades, in order to reach competitive advantage in their application domain. Many contributions have arisen from the industrial context to service-oriented companies, for instance, in the scope of artificial intelligence. Therefore, many contributions have been put forward related to collaborative and intelligent networks and systems.

In spite of the wide range of existing work in this area, however, it continues to be imperative for companies to understand and anticipate the importance of CNS in manufacturing to enable them to reach a competitive advantage in the current global market and Industry-4.0-oriented scenario.

These main topics strengthen the specific characteristics of CN through collaboration to deliver products and services; decentralization of decision-making; and inter- and intra-organizational integration to meet imposed performance requirements in competitive global markets.

Moreover, in the context of CNS, normalization is a crucial step in all decision models, to produce comparable and dimension less data from heterogeneous data. Therefore, it is of upmost importance to use appropriate data normalization techniques for each application scenario, for instance, according to the kind of multicriteria or multiobjective optimization methods or algorithms used for networked supply and operations management. This is even more important in the upcoming increasingly digital era of the I4.0, along with the perceived need for big data processing, regarding the need for vertical and horizontal integration of data and manufacturing processes.

This Special Issue intends to provide a contribution to the domain of collaborative and intelligent networks and systems for supporting engineering and production management to fill the gap in theories and practical applications for supporting industrial companies through suitable methods and solutions applicable to a wide range of instances. Therefore, this Special Issue aims to bring together researchers from a wide range of disciplines to provide potential contributions to the main topics underlying this proposal, although not limited to the following:

- Collaboration strategies;

- Intelligent models, methods, and tools;

- Dynamic and real-time based decision-support approaches;

- Decentralized decision-support networks;

- Hybrid intelligent decision-support systems;

- Multiagents;

- Machine learning;

- Bio-inspired models and algorithms;

- Negotiation and group decision-making approaches;

- Multicriteria and multiobjective models;

- Uncertainty treatment;

- Data normalization and data fusion methods and techniques;

- Data analytics for manufacturing systems and processes;

- Cloud computing and big data;

- Learning and data mining;

- Data visualization for the digital factory;

- Real time machine and process monitoring, diagnostics, and prognostics;

- Real-time management;

- Manufacturing execution systems;

- Open source software applications for digital or cyber manufacturing;

- Internet of Things for cyber manufacturing.

Dr. Leonilde Varela
Dr. Goran D. Putnik
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. Future Internet 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 1400 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

  • collaborative and intelligent networks and systems
  • Industry 4.0
  • cyber-physical systems
  • real-time-based decision making

Published Papers (4 papers)

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Research

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Article
Co-Authorship Networks Analysis to Discover Collaboration Patterns among Italian Researchers
Future Internet 2022, 14(6), 187; https://doi.org/10.3390/fi14060187 - 16 Jun 2022
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Abstract
The study of the behaviors of large community of researchers and what correlations exist between their environment, such as grouping rules by law or specific institution policies, and their performance is an important topic since it affects the metrics used to evaluate the [...] Read more.
The study of the behaviors of large community of researchers and what correlations exist between their environment, such as grouping rules by law or specific institution policies, and their performance is an important topic since it affects the metrics used to evaluate the quality of the research. Moreover, in several countries, such as Italy, these metrics are also used to define the recruitment and funding policies. To effectively study these topics, we created a procedure that allow us to craft a large dataset of Italian Academic researchers, having the most important performance indices together with co-authorships information, mixing data extracted from the official list of academic researchers provided by Italian Ministry of University and Research and the Elsevier’s Scopus database. In this paper, we discuss our approach to automate the process of correct association of profiles and the mapping of publications reducing the use of computational resources. We also present the characteristics of four datasets related to specific research fields defined by the Italian Ministry of University and Research used to group the Italian researchers. Then, we present several examples of how the information extracted from these datasets can help to achieve a better understanding of the dynamics influencing scientist performances. Full article
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Article
Proposal for a System Model for Offline Seismic Event Detection in Colombia
Future Internet 2020, 12(12), 231; https://doi.org/10.3390/fi12120231 - 18 Dec 2020
Viewed by 1303
Abstract
This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection [...] Read more.
This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system. Full article
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Article
Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry
Future Internet 2020, 12(9), 159; https://doi.org/10.3390/fi12090159 - 22 Sep 2020
Cited by 5 | Viewed by 1177
Abstract
Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as [...] Read more.
Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as transport (including the aeronautical, automotive and railway industries), and energy or biomedical industries. As in the case of many other manufacturing technologies, grinding relies heavily on the experience and knowledge of the operatives. For this reason, considerable efforts have been devoted to generating a systematic and sustainable approach that reduces and eventually eliminates costly trial-and-error strategies. The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The structure of the DT comprises four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for process models; and (4) knowledge integration in a configurable open-source industrial tool. To this end, intensive collaboration between all the involved agents (from university to industry) is essential. One of the most remarkable results is the development of new and more realistic models for predicting wheel wear, which currently can only be known in industry through costly trial-and-error strategies. Also, current work is focused on the development of an intelligent grinding wheel, which will provide on-line information about process variables such as temperature and forces. This is a critical issue in the advance towards a zero-defect grinding process. Full article
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Review

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Review
Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review
Future Internet 2021, 13(3), 57; https://doi.org/10.3390/fi13030057 - 25 Feb 2021
Cited by 2 | Viewed by 1028
Abstract
Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the [...] Read more.
Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS. Full article
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