Academic Journal
New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II
| Title: | New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II |
|---|---|
| Authors: | Luis Norberto López de Lacalle, Jorge Posada |
| Source: | Applied Sciences, Vol 12, Iss 15, p 7952 (2022) |
| Publisher Information: | MDPI AG, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | n/a, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | The second volume of the Special Issue New Industry 4 [...] |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/15/7952; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app12157952 |
| Access URL: | https://doaj.org/article/9c44921f87b440fe885df1e3e2c33824 |
| Accession Number: | edsdoj.9c44921f87b440fe885df1e3e2c33824 |
| Database: | Directory of Open Access Journals |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHjPtM4BHU3ZchRwgzYmadcigk49r9CVlbU7V5F6lgH7WwHib162LlxoFaI39bqcIkfxAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDB4nPx6xsuRMoTK10AIBEICBmjm-B8uNIOKqmKKaQLFTwFJgVVLNz2zGTCW5h6EtQddGHAh0xkZ7VTexyOFIF0JdqzrBNC8c-iwHvwRwwp-TPCCSj1ELHkWn73pz6SI9iGhRiD8nVSRDyy_kR25XtVTDj9_v7thhjPVb1y0oLSTrGkY2wyf98GoLbTa4GK6wAgNjC8o65otYL4L9Cl4yWj6Ll9-t_D8k2Ln5c4k= Text: Availability: 1 Value: <anid>AN0158523163;[fdtu]01aug.22;2022Aug18.08:29;v2.2.500</anid> <title id="AN0158523163-1">New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II </title> <p>The second volume of the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes is now closed with 17 interesting research contributions and 1 review. Two years since the previous Special Issue, industrial factories have been experiencing a rapid digital transformation because of the introduction of emerging ICT technologies, such as the industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, artificial intelligence, intelligent robotics, cyber–physical systems, digital twins and visual computing (including augmented reality, visual analytics, cognitive computer vision, and new HMI interfaces and simulation and computer graphics), among others. This is evident in the global trend of Industry 4.0 and related initiatives, which are present in one way or another in many different production strategies at an international level. In recent times, the term Industry 5.0 has been used to strength the meaning of the influence of human-centric manufacturing and sustainability.</p> <p>Both classical and new manufacturing processes are being enhanced by the use of big data analytics on industrial sensor data. In the current machine tools and systems, there are complex sensors that are able to gather useful information, which can be captured, stored, and processed with edge, fog, or cloud computing technologies. Manufacturing process modelling can lead to improvements in productivity and quality and, in several cases, are implemented by means of digital twins on cyber–physical production devices and systems.</p> <p>In this line, manufacturing process models (e.g., thermal, vibration, deformation) can be improved with digital monitoring, digital twins, visual data analytics, artificial intelligence, and computer vision in order to achieve a more productive and reliable smart factory.</p> <p>On the other hand, the role of the human factor is absolutely fundamental in these new paradigms. Collaborative robots are spreading in several applications in order to work along with human skillful workers. New approaches for augmented reality and immersive virtual reality, as well as other multimodal ways of improving human computer interaction in manufacturing scenarios, are enhancing the capabilities of operators and engineers so as to capture and reproduce human knowledge, improve their performance in operational tasks, and seamlessly integrate their valuable experience and flexibility in smart factory scenarios for manufacturing. Visual analytics can help in decision-making by management, domain experts, operators, engineers, and so on, by providing user-specific interactive visualization and the exploration of operational data in combination with machine learning approaches.</p> <p>Regarding the Special Issue contributions, Červeňanská et al. [[<reflink idref="bib1" id="ref1">1</reflink>]] addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. Sasian et al. [[<reflink idref="bib2" id="ref2">2</reflink>]] focused on the influence of new 5G networks in factories; 5 g and field buses are key enabling technologies. Ojstersek et al. [[<reflink idref="bib3" id="ref3">3</reflink>]] makes a contribution based on three-dimensional modelling made from capturing spherical camera data. Edge-computing devices and architectures are currently being implemented in factories; this is the context of contributions [[<reflink idref="bib4" id="ref4">4</reflink>], [<reflink idref="bib6" id="ref5">6</reflink>]]. Redondo et al. [[<reflink idref="bib7" id="ref6">7</reflink>]] aims at hybrid unsupervised exploratory plots (HUEPs) as a visualization technique that combines exploratory projection pursuit (EPP) and clustering methods. Erasmus et al. [[<reflink idref="bib8" id="ref7">8</reflink>]] summarizes the so-called HORSE Project, investigating several of the new technologies to find novel ways to improve the flexibility as part of the Horizon 2020 research and innovation program. Serras et al. [[<reflink idref="bib9" id="ref8">9</reflink>]] introduced extended reality (XR) technologies (such as virtual, augmented, immersive, and mixed reality), with a focus on speech and AR interaction complementary to the work of Simoes et al. [[<reflink idref="bib10" id="ref9">10</reflink>]], as is the case in Kim et al. [[<reflink idref="bib12" id="ref10">12</reflink>]], who present a new data-augmentation method.</p> <p>In the work [[<reflink idref="bib13" id="ref11">13</reflink>]], Mejia-Parra et al. present four different schemes that translate the problem of laser heating of rectangular plates into equivalent FFT problem. The presented schemes make use of the FFT algorithm to reduce the computational time complexity of the problem, improving his previous work in [[<reflink idref="bib14" id="ref12">14</reflink>]].</p> <p>The authors of contributions [[<reflink idref="bib15" id="ref13">15</reflink>], [<reflink idref="bib17" id="ref14">17</reflink>]] introduced algorithms and applications in the field of machine learning, a classic effort nowadays because artificial neural networks or Markov nets, etc., help to solve problems in manufacturing. In [[<reflink idref="bib18" id="ref15">18</reflink>], [<reflink idref="bib20" id="ref16">20</reflink>]], three applications are shown. The closing work by Prinsloo et al. [[<reflink idref="bib21" id="ref17">21</reflink>]] is a review about cyber-security risks because the Internet and connectivity are key in automated systems.</p> <p>The future will bring more challenges and opportunities; in fact, digitalization is a global trend with multiple possibilities, and a Special Issue is only a humble attempt to go a step beyond, adding new ideas to other approaches, such those in the previous Special Issue [[<reflink idref="bib22" id="ref18">22</reflink>]], or other related works in European projects [[<reflink idref="bib23" id="ref19">23</reflink>]]. In the not-so-distant future, factory workers will be helped by new digital twins, utilities, and software toolboxes to improve the production quality, productivity, and health of workers. Other related Special Issues are [[<reflink idref="bib24" id="ref20">24</reflink>]], where Industry 4.0 technologies are now a hot topic.</p> <hd id="AN0158523163-2">Author Contributions</hd> <p>All authors are special issue editors. All authors have read and agreed to the published version of the manuscript.</p> <hd id="AN0158523163-3">Conflicts of Interest</hd> <p>The author declares no conflict of interest.</p> <ref id="AN0158523163-4"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.</bibtext> </blist> </ref> <ref id="AN0158523163-5"> <title> References </title> <blist> <bibtext> Červeňanská Z., Kotianová J., Važan P., Juhásová B., Juhás M. Multi-Objective Optimization of Production Objectives ase don Surrogate Model. Appl. Sci. 2020; 107870. 10.3390/app10217870</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Sasiain J., Sanz A., Astorga J., Jacob E. Towards Flexible Integration of 5G and IioT Technologies in Industry 4.0: A Practical Use Case. Appl. Sci. 2020; 107670. 10.3390/app10217670</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Ojstersek R., Buchmeister B., Vujica Herzog N. Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation. Appl. Sci. 2020; 107037. 10.3390/app10207037</bibtext> </blist> <blist> <bibl id="bib4" idref="ref4" type="bt">4</bibl> <bibtext> Minchala L.I., Peralta J., Mata-Quevedo P., Rojas J. An Approach to Industrial Automation based on Low-Cost Embedded Platforms and Open Software. Appl. Sci. 2020; 104696. 10.3390/app10144696</bibtext> </blist> <blist> <bibl id="bib5" type="bt">5</bibl> <bibtext> Ougaabal K., Zacharewicz G., Ducq Y., Tazi S. Visual Workflow Process Modeling and Simulation Approach ase don Non-Functional Properties of Resources. Appl. Sci. 2020; 104664. 10.3390/app10134664</bibtext> </blist> <blist> <bibl id="bib6" idref="ref5" type="bt">6</bibl> <bibtext> Garrido-Labrador J.L., Puente-Gabarri D., Ramírez-Sanz J.M., Ayala-Dulanto D., Maudes J. Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution. Appl. Sci. 2020; 104606. 10.3390/app10134606</bibtext> </blist> <blist> <bibl id="bib7" idref="ref6" type="bt">7</bibl> <bibtext> Redondo R., Herrero Á., Corchado E., Sedano J. A Decision-Making Tool ase don Exploratory Visualization for the Automotive Industry. Appl. Sci. 2020; 104355. 10.3390/app10124355</bibtext> </blist> <blist> <bibl id="bib8" idref="ref7" type="bt">8</bibl> <bibtext> Erasmus J., Vanderfeesten I., Traganos K., Keulen R., Grefen P. The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing. Appl. 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Sci. 2020; 103755. 10.3390/app10113755</bibtext> </blist> <blist> <bibtext> Mejia-Parra D., Arbelaiz A., Ruiz-Salguero O., Lalinde-Pulido J., Moreno A., Posada J. Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware. Appl. Sci. 2020; 103281. 10.3390/app10093281</bibtext> </blist> <blist> <bibtext> Mejia D., Moreno A., Arbelaiz A., Posada J., Ruiz-Salguero O., Chopitea R. Accelerated Thermal Simulation for Three-Dimensional Interactive Optimization of Computer Numeric Control Sheet Metal Laser Cutting. J. Manuf. Sci. Eng. 2018; 140: 31006. 10.1115/1.4038207</bibtext> </blist> <blist> <bibtext> Chen S., Fang S., Tang R. An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing. Appl. Sci. 2020; 102491. 10.3390/app10072491</bibtext> </blist> <blist> <bibtext> Chen C.-N., Liu T.-K., Chen Y.J. Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net. Appl. 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Sci. 2019; 94983. 10.3390/app9234983</bibtext> </blist> <blist> <bibtext> Prinsloo J., Sinha S., von Solms B. A Review of Industry 4.0 Manufacturing Process Security Risks. Appl. Sci. 2019; 95105. 10.3390/app9235105</bibtext> </blist> <blist> <bibtext> De Lacalle L.N.L., Posada J. Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes. Appl. Sci. 2019; 94323. 10.3390/app9204323</bibtext> </blist> <blist> <bibtext> Del Olmo A., de Lacalle L.L., de Pissón G.M., Pérez-Salinas C., Ealo J.A., Sastoque L., Fernandes M.H. Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors. Mech. Syst. Signal Process. 2022; 172: 109003. 10.1016/j.ymssp.2022.109003</bibtext> </blist> <blist> <bibtext> Zambon I., Egidi G., Rinaldi F., Cividino S. Applied Research Towards Industry 4.0: Opportunities for SMEs. Processes. 2019; 7344. 10.3390/pr7060344</bibtext> </blist> <blist> <bibtext> Papakostas N., Constantinescu C., Mourtzis D. Novel Industry 4.0 Technologies and Applications. Appl. Sci. 2020; 106498. 10.3390/app10186498</bibtext> </blist> </ref> <aug> <p>By Luis Norberto López de Lacalle and Jorge Posada</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref9"></nolink> <nolink nlid="nl2" bibid="bib12" firstref="ref10"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref11"></nolink> <nolink nlid="nl4" bibid="bib14" firstref="ref12"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref13"></nolink> <nolink nlid="nl6" bibid="bib17" firstref="ref14"></nolink> <nolink nlid="nl7" bibid="bib18" firstref="ref15"></nolink> <nolink nlid="nl8" bibid="bib20" firstref="ref16"></nolink> <nolink nlid="nl9" bibid="bib21" firstref="ref17"></nolink> <nolink nlid="nl10" bibid="bib22" firstref="ref18"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref19"></nolink> <nolink nlid="nl12" bibid="bib24" firstref="ref20"></nolink> CustomLinks: – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=20763417&ISBN=&volume=12&issue=15&date=20220801&spage=7952&pages=7952-7952&title=Applied Sciences&atitle=New%20Industry%204.0%20Advances%20in%20Industrial%20IoT%20and%20Visual%20Computing%20for%20Manufacturing%20Processes%3A%20Volume%20II&aulast=Luis%20Norberto%20L%C3%B3pez%20de%20Lacalle&id=DOI:10.3390/app12157952 Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries – Url: https://doaj.org/article/9c44921f87b440fe885df1e3e2c33824 Name: EDS - DOAJ (s8985755) Category: fullText Text: View record from DOAJ MouseOverText: View record from DOAJ |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app12157952 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: 7952 Subjects: – SubjectFull: n/a Type: general – SubjectFull: Technology Type: general – SubjectFull: Engineering (General). Civil engineering (General) Type: general – SubjectFull: TA1-2040 Type: general – SubjectFull: Biology (General) Type: general – SubjectFull: QH301-705.5 Type: general – SubjectFull: Physics Type: general – SubjectFull: QC1-999 Type: general – SubjectFull: Chemistry Type: general – SubjectFull: QD1-999 Type: general Titles: – TitleFull: New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luis Norberto López de Lacalle – PersonEntity: Name: NameFull: Jorge Posada IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 12 – Type: issue Value: 15 Titles: – TitleFull: Applied Sciences Type: main |
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