Digital Transformations to Design, Implement and Operate Cyber-Physical Systems in Smart Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 1771

Special Issue Editors


E-Mail Website
Guest Editor
Tecnologico de Monterrey, School of Engineering and Science, Monterrey 64849, Mexico
Interests: smart manufacturing; mechanical design; powertrains in electromobility

E-Mail Website
Guest Editor
Tecnologico de Monterrey, School of Engineering and Science, Monterrey 64849, Mexico
Interests: manufacturing system; automotive engineering; mechanical design and precision engineering

Special Issue Information

Dear Colleagues,

On a daily basis, there are a great number of advances in manufacturing systems around the world. However, their effect and the characteristics of their implementation represent a unique source of creativity that can open the door to the small and medium enterprises (SME) to accelerate the incursion in digital manufacturing. The technical aspects of the way in which technologies like the digital twins are produced and applied remain a subject of significant research. Although industry in general is moving towards the next evolution of automation, the world has recognized the importance of human-centered, sustainable, and resilient manufacturing. Digital transformation has enabled the mass personalization of products, and at the same time, the tracking and traceability along the entire life cycle. The landscape of tools at reach includes the hybrid manipulation of virtual and physical entities and its role in the entire cyber-physical system.

With the above in mind, this Special Issue will provide a forum for researchers and practitioners to exchange their latest theoretical and engineering achievements and identify critical issues and challenges for future studies in the digital manufacturing systems and the related tools, models, and technologies. Results of experimental research in field conditions are encouraged for submission. The theoretical papers and proofs of concepts accepted into this Special Issue are expected to contain original ideas and potential solutions for resolving real problems.

Dr. Pedro Daniel Urbina-Coronado
Dr. Horacio Ahuett-Garza
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. Machines 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 2400 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
  • Industry 5.0
  • digital twin
  • cyber-physical systems
  • digital threads
  • factory simulation
  • augmented/virtual/mixed reality
  • machine learning
  • additive manufacturing
  • 4D printing modelling

Published Papers (1 paper)

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Research

14 pages, 2251 KiB  
Article
Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network
by Min Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim and Jong-Ho Shin
Machines 2023, 11(12), 1042; https://doi.org/10.3390/machines11121042 - 23 Nov 2023
Viewed by 1330
Abstract
Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants [...] Read more.
Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively. Full article
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