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Information
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Knowledge Engineering in Industry 4.0
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Knowledge Engineering in Industry 4.0

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 18652

Special Issue Editors

Dr. Matteo Cristani

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Guest Editor
Department of Computer Science, University of Verona, 37129 Verona, Italy
Interests: artificial intelligence; knowledge representation; machine learning; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gordan Ježić

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Guest Editor
Department of Telecommunications, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Interests: multiple agent systems; communication technologies; next generation computers
Prof. Dr. Cecilia Zanni-Merk

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Guest Editor
INSA Rouen Normandie, Normandie Université, LITIS (UR 4108/FR CNRS 3638), F-76000 Rouen, France
Interests: knowledge engineering; conceptualisation; ontologies and knowledge graphs; rule-based reasoning (crisp, fuzzy, probabilistic, spatio-temporal); case-based reasoning; knowledge and experience capitalisation; semantic web tech-nologies; explainability
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Toro

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Guest Editor
Vicomtech Research Centre, 20009 San Sebastian, Spain
Interests: industry 4.0; visual computing; smart factories
Dr. Claudio Tomazzoli

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Guest Editor
Department of Computer Science, University of Verona, 37129 Verona, Italy
Interests: artificial intelligence; knowledge representation; machine learning; multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 has been developing as a paradigm of novel industrial revolution and has posed the base for a wider view of the development of many different and, so far, distinct segments of research in computer science and information technology. There is an increasing demand for knowledge-driven approaches to be applied in the fields of interest of the Industry 4.0 concept, for at least three basic reasons: (a) for the strong interaction between the logic/symbolic approach to knowledge engineering and the design patterns methods applied to the development of sophisticated Industry 4.0 solutions; (b) for the increasingly visible trend of integration between information technologies and artificial intelligence, so that we can talk about artificial intelligence and IoT; (c) for the strong background issues of engineering required when searching for real-world solutions to the development of novel methods in Industry 4.0.

For this reason, we have conceived this Special Issue, whose purpose is to gather the many researchers operating in the field to contribute to a collective effort in understanding the trends and future questions in the field. Topics include but are not limited to:

- Interconnection among knowledge-based components of Industry 4.0 solutions;

- Knowledge transparency;

- Interconnectivity of knowledge, software, and hardware components;

- Knowledge-driven technical assistance services on board of industrial plants;

- Knowledge-decentralized decisions;

- Knowledge components of mobile devices;

- IoT knowledge-based platforms;

- Intelligent location detection technologies;

- Intelligent smart sensors;

- Big knowledge analytics;

- Cognitive computing.

Dr. Matteo Cristani
Prof. Dr. Gordan Ježić
Prof. Dr. Cecilia Zanni-Merk
Dr. Carlos Toro
Dr. Claudio Tomazzoli
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. Information 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 1600 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

  • Industry 4.0
  • Knowledge engineering
  • Cognitive robotics
  • Ambient intelligence

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Published Papers (5 papers)

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15 pages, 5123 KiB  
Article
Methodology and Tools to Integrate Industry 4.0 CPS into Process Design and Management: ISA-88 Use Case
by Ander Garcia, Xabier Oregui, Unai Arrieta and Iñigo Valverde
Information 2022, 13(5), 226; https://doi.org/10.3390/info13050226 - 28 Apr 2022
Cited by 4 | Viewed by 4424
Abstract
Once an industrial process is designed, the real implementation of the process control is programmed into Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) devices on the shop floor. These devices are programmed with their low-level coding languages. This presents [...] Read more.
Once an industrial process is designed, the real implementation of the process control is programmed into Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) devices on the shop floor. These devices are programmed with their low-level coding languages. This presents several drawbacks, such as inconsistencies and naming errors between the design and the implementation steps, or difficulties in integrating new Industry 4.0 functionalities. This paper presents a design and management methodology, and a software architecture to overcome these drawbacks. The objective of the methodology is the interconnectivity of domain knowledge, software, and hardware components to automatically generate Industry 4.0 Cyber-Physical Systems (CPS) for industrial processes. The methodology is composed of five main steps: designing the process, programming the PLC, capturing data, managing the process and visualizing it. Based on the methodology and the architecture, a set of tools targeting ISA-88 processes has been implemented and validated. IEC-61512 (also known as ANSI/ISA-88) is a standard addressing batch process control. It follows a design philosophy for describing equipment and procedures, equally applicable to manual processes. The methodology has been validated on a process controlled by a Siemens 1200 PLC. The main advantages of this methodology identified during the validation are: (i) reduction in the time required to design the ISA-88 process, (ii) reduction in the PLC programming time and associated errors, (iii) automatic integration of a CPS with data capture and visualization functionalities, and (iv) remote management of the process. Full article
(This article belongs to the Special Issue Knowledge Engineering in Industry 4.0)
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15 pages, 857 KiB  
Article
A Rule-Based Heuristic Methodology for Su-Field Analysis in Industrial Engineering Design
by Wei Yan, Cecilia Zanni-Merk, Denis Cavallucci, Qiushi Cao, Liang Zhang and Zengyan Ji
Information 2022, 13(3), 143; https://doi.org/10.3390/info13030143 - 8 Mar 2022
Cited by 1 | Viewed by 2680
Abstract
Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been [...] Read more.
Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been used to effectively improve the performance of industrial systems. However, the inventive standards used for engineering design are summarized and classified according to a large number of patents in different fields. They are built on a highly abstract basis and are independent of specific application fields, making their use require much more technical knowledge than other TRIZ tools. To facilitate the use of invention standards, in particular to capture the uncertainty or imprecision described in the standards, this paper proposes a rule-based heuristic approach. First, Su-Field analysis ontology and fuzzy analysis ontology are constructed to represent precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) reasoning and fuzzy reasoning are executed to generate heuristic conceptual solutions. Finally, we develop a software prototype and elaborate the resolution of “Auguste Piccard’s Stratostat ” in the prototype. Full article
(This article belongs to the Special Issue Knowledge Engineering in Industry 4.0)
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16 pages, 3852 KiB  
Article
Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms
by Unai Elordi, Chiara Lunerti, Luis Unzueta, Jon Goenetxea, Nerea Aranjuelo, Alvaro Bertelsen and Ignacio Arganda-Carreras
Information 2021, 12(12), 532; https://doi.org/10.3390/info12120532 - 20 Dec 2021
Cited by 3 | Viewed by 3715
Abstract
In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, [...] Read more.
In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices. Full article
(This article belongs to the Special Issue Knowledge Engineering in Industry 4.0)
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14 pages, 5568 KiB  
Article
An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning
by Fátima A. Saiz, Garazi Alfaro and Iñigo Barandiaran
Information 2021, 12(12), 489; https://doi.org/10.3390/info12120489 - 23 Nov 2021
Cited by 12 | Viewed by 3373
Abstract
This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of [...] Read more.
This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performance when used individually and when using an ensemble of them is carried out, obtaining an improvement of 7% in accuracy in the ensemble. The results of the test set demonstrate the successful performance of the system in terms of component classification. Full article
(This article belongs to the Special Issue Knowledge Engineering in Industry 4.0)
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18 pages, 6688 KiB  
Article
SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0
by Sadeer Beden, Qiushi Cao and Arnold Beckmann
Information 2021, 12(8), 304; https://doi.org/10.3390/info12080304 - 29 Jul 2021
Cited by 6 | Viewed by 2984
Abstract
This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and [...] Read more.
This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel. Full article
(This article belongs to the Special Issue Knowledge Engineering in Industry 4.0)
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