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New Industry 4.0 Advances in Industrial IoT and Visual Computing...
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New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 22534
Related Special Issue: New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II and New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

Special Issue Editors

Prof. Dr. Luis Norberto López De Lacalle

E-Mail Website
Guest Editor
The Aeronautics Advanced Manufacturing Center-CFAA, 48170 Zamudio, Biscay, Spain
Interests: manufacturing process
Special Issues, Collections and Topics in MDPI journals
Dr. Jorge Posada

E-Mail Website
Guest Editor
Vicomtech Technological Center, Paseo Mikeletegi 57, E-20009 Donostia/San Sebastián, Spain
Interests: Industry 4.0; visual computing; computer graphics; simulation; knowledge engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last three years, 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, 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 (Industrie 4.0, Germany; industrial Internet, USA; Industrie du Futur, France; made in China 2025, China; etc.).

In the context of high performance manufacturing, the impact of these technologies is clear. Important improvements can be achieved in productivity, systems reliability, parts quality, and human welfare.

Both classical and new manufacturing processes (such as additive manufacturing), based on advanced mechanical principles, 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 processes modeling 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.

In line with this, 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.

On the other hand, the role of the human factor is absolutely fundamental in these new paradigms. The use of collaborative robots is increasing in several applications in order to work alongside skilled human 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.

In summary, this Special Issue is an opportunity for the scientific community to present recent research regarding industrial IoT and visual computing as key aspects of Industry 4.0 for manufacturing processes.

Prof. Dr. Luis Norberto López De Lacalle
Dr. Jorge Posada
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • Advanced manufacturing
  • Industry 4.0
  • Smart factories
  • Visual computing
  • Industrial Internet of Things
  • Cyber physical systems, and cyber-physical production systems
  • Digital twins
  • Edge, fog, and cloud computing
  • Augmented reality
  • 5G in manufacturing
  • Deep analytics
  • Industrial big data
  • Workshop networks
  • High performance manufacturing
  • Manufacturing processes
  • Machine and processes monitoring
  • Knowledge-based manufacturing
  • Advances in manufacturing processes
  • Process modeling, process simulation
  • Virtual manufacturing
  • Artificial vision
  • Virtual reality
  • Collaborative robots
  • Management in new digitally powered manufacturing concepts

Published Papers (5 papers)

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Research

18 pages, 5733 KiB  
Article
Conceiving a Digital Twin for a Flexible Manufacturing System
by Laurence C. Magalhães, Luciano C. Magalhães, Jhonatan B. Ramos, Luciano R. Moura, Renato E. N. de Moraes, João B. Gonçalves, Wilian H. Hisatugu, Marcelo T. Souza, Luis N. L. de Lacalle and João C. E. Ferreira
Appl. Sci. 2022, 12(19), 9864; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199864 - 30 Sep 2022
Cited by 22 | Viewed by 2727
Abstract
Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life [...] Read more.
Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III)
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22 pages, 8610 KiB  
Article
Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
by David Checa, Juan José Saucedo-Dorantes, Roque Alfredo Osornio-Rios, José Alfonso Antonino-Daviu and Andrés Bustillo
Appl. Sci. 2022, 12(1), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010414 - 01 Jan 2022
Cited by 12 | Viewed by 3132
Abstract
The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. [...] Read more.
The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III)
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20 pages, 5774 KiB  
Article
A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
by Alim Yasin, Toh Yen Pang, Chi-Tsun Cheng and Miro Miletic
Appl. Sci. 2021, 11(20), 9479; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209479 - 12 Oct 2021
Cited by 13 | Viewed by 4423
Abstract
In the last decade, Australian SMEs are steadily becoming more digitally engaged, but they still face issues and barriers to fully adopt Industry 4.0 (I4.0). Among the tools that I4.0 encompasses, digital twin (DT) and digital thread (DTH) technologies hold significant interest and [...] Read more.
In the last decade, Australian SMEs are steadily becoming more digitally engaged, but they still face issues and barriers to fully adopt Industry 4.0 (I4.0). Among the tools that I4.0 encompasses, digital twin (DT) and digital thread (DTH) technologies hold significant interest and value. Some of the challenges are the lack of expertise in developing the communication framework required for data collection, processing, and storing; concerns about data and cyber security; lack of knowledge of the digitization and visualisation of data; and value generation for businesses from the data. This article aims to demonstrate the feasibility of DT implementation for small and medium-sized enterprises (SMEs) by developing a framework based on simple and low-cost solutions and providing insight and guidance to overcome technological barriers. To do so, this paper first outlines the theoretical framework and its components, and subsequently discusses a simplified and generalised DT model of a real-world physical asset that demonstrates how these components function, how they are integrated and how they interact with each other. An experimental scenario is presented to transform data harvested from a resistance temperature detector sensor connected with a WAGO 750-8102 Programmable Logic Controller for data storage and analysis, predictive simulation and modelling. Our results demonstrate that sensor data could be readily integrated from Internet-of-Things (IoT) devices and enabling DT technologies, where users could view real time data and key performance indicators (KPIs) in the form of a 3D model. Data from both the sensor and 3D model are viewable in a comprehensive history log through a database. Via this technological demonstration, we provide several recommendations on software, hardware, and expertise that SMEs may adopt to assist with their DT implementations. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III)
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17 pages, 4406 KiB  
Article
Adaptive Data Augmentation to Achieve Noise Robustness and Overcome Data Deficiency for Deep Learning
by Eunkyeong Kim, Jinyong Kim, Hansoo Lee and Sungshin Kim
Appl. Sci. 2021, 11(12), 5586; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125586 - 17 Jun 2021
Cited by 9 | Viewed by 2001
Abstract
Artificial intelligence technologies and robot vision systems are core technologies in smart factories. Currently, there is scholarly interest in automatic data feature extraction in smart factories using deep learning networks. However, sufficient training data are required to train these networks. In addition, barely [...] Read more.
Artificial intelligence technologies and robot vision systems are core technologies in smart factories. Currently, there is scholarly interest in automatic data feature extraction in smart factories using deep learning networks. However, sufficient training data are required to train these networks. In addition, barely perceptible noise can affect classification accuracy. Therefore, to increase the amount of training data and achieve robustness against noise attacks, a data augmentation method implemented using the adaptive inverse peak signal-to-noise ratio was developed in this study to consider the influence of the color characteristics of the training images. This method was used to automatically determine the optimal perturbation range of the color perturbation method for generating images using weights based on the characteristics of the training images. The experimental results showed that the proposed method could generate new training images from original images, classify noisy images with greater accuracy, and generally improve the classification accuracy. This demonstrates that the proposed method is effective and robust to noise, even when the training data are deficient. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III)
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19 pages, 2363 KiB  
Article
Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
by Lu Wang, Dongkai Zhang, Jiahao Guo and Yuexing Han
Appl. Sci. 2020, 10(23), 8660; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238660 - 03 Dec 2020
Cited by 31 | Viewed by 8673
Abstract
Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard [...] Read more.
Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III)
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