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 December 2021.

Special Issue Editors

Dr. Leonilde Varela
Website SciProfiles
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 and Collections in MDPI journals
Dr. Goran D. Putnik
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 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. 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 1000 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 (1 paper)

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Research

Open AccessArticle
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
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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