Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
New Industry 4.0 Advances in Industrial IoT and Visual Computing...
share announcement

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

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 September 2020) | Viewed by 78048
Related Special Issue: New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III

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 the productivity, systems reliability, parts quality, and human welfare.

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

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 skilful 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 López de Lacalle
Dr.-Ing. 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 (19 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

3 pages, 206 KiB  
Editorial
New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II
by Luis Norberto López de Lacalle and Jorge Posada
Appl. Sci. 2022, 12(15), 7952; https://doi.org/10.3390/app12157952 - 08 Aug 2022
Cited by 1 | Viewed by 1213
Abstract
The second volume of the Special Issue New Industry 4 [...] Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)

Research

Jump to: Editorial, Review

18 pages, 2679 KiB  
Article
Multi-Objective Optimization of Production Objectives Based on Surrogate Model
by Zuzana Červeňanská, Janette Kotianová, Pavel Važan, Bohuslava Juhásová and Martin Juhás
Appl. Sci. 2020, 10(21), 7870; https://doi.org/10.3390/app10217870 - 06 Nov 2020
Cited by 4 | Viewed by 1989
Abstract
The article addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. We employ the surrogate of the discrete-event simulation model of a batch production system in an analytical form. Integration of simulation, Design of Experiments [...] Read more.
The article addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. We employ the surrogate of the discrete-event simulation model of a batch production system in an analytical form. Integration of simulation, Design of Experiments methods, and Weighted Sum and Weighted Product multi-objective methods are used in an arrangement of a priori defined preferences to find a solution near the Pareto optimal solution in a criterion space. We compare the results obtained through the analytical approach to the outcomes of simulation-based optimization. The observed results indicate a possibility to apply the suitable analytical model for quickly finding the acceptable approximate solution close to the Pareto optimal front. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

20 pages, 2410 KiB  
Article
Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
by Jorge Sasiain, Ane Sanz, Jasone Astorga and Eduardo Jacob
Appl. Sci. 2020, 10(21), 7670; https://doi.org/10.3390/app10217670 - 29 Oct 2020
Cited by 27 | Viewed by 4890
Abstract
The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, [...] Read more.
The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, 5G also presents itself as a key enabler to fulfill Industry 4.0 requirements. In this article, the authors first propose a 5G-enabled architecture for Industry 4.0. Smart Networks for Industry (SN4I) is introduced, an experimental facility based on two 5G key-enabling technologies—Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)—which connects the University of the Basque Country’s Aeronautics Advanced Manufacturing Center and Faculty of Engineering in Bilbao. Then, the authors present the deployment of a Wireless Sensor Network (WSN) with strong access control mechanisms into such architecture, enabling secure and flexible Industrial Internet of Things (IIoT) applications. Additionally, the authors demonstrate the implementation of a use case consisting in the monitoring of a broaching process that makes use of machine tools located in the manufacturing center, and of services from the proposed architecture. The authors finally highlight the benefits achieved regarding flexibility, efficiency, and security within the presented scenario and to the manufacturing industry overall. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

17 pages, 4102 KiB  
Article
Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation
by Robert Ojstersek, Borut Buchmeister and Natasa Vujica Herzog
Appl. Sci. 2020, 10(20), 7037; https://doi.org/10.3390/app10207037 - 10 Oct 2020
Cited by 7 | Viewed by 3282
Abstract
In the time of Industry 4.0, the dynamic adaptation of companies to global market demands plays a key role in ensuring sustainable financial and time justification. Financial accessibility, a wide range of user-friendliness, and credible results of the visual computing methods and data-driven [...] Read more.
In the time of Industry 4.0, the dynamic adaptation of companies to global market demands plays a key role in ensuring sustainable financial and time justification. Financial accessibility, a wide range of user-friendliness, and credible results of the visual computing methods and data-driven simulation modeling enable a higher degree of usability in small, medium, and large enterprises. This paper presents an innovative method for modelling and simulating workplaces in manufacturing based on visual data captured with a spherical camera. The presented approach uses simulation scenarios to investigate the optimization of manual or collaborative workplaces. We evaluated and compared three simulated scenarios, the results of which highlight the potential for improvement regarding manufacturing productivity and cost. In addition, ergonomic analyses of a manual assembly workplace were performed using existing evaluation metrics. The results show the possibility of creating a three-dimensional model of a workplace captured with a spherical camera, which not only describes the model dimensionally but also adds terminological and other production parameters obtained through the analysis of manufacturing system videos. The confirmation of the appropriateness of introducing collaborative workstations is also confirmed by ergonomic analyses Ovaco working analyzing system (OWAS) and rapid upper limb assessment (RULA), which demonstrate the sustainable limits of manual assembly workplaces. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

15 pages, 4274 KiB  
Article
An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
by Luis I. Minchala, Jonnathan Peralta, Paul Mata-Quevedo and Jaime Rojas
Appl. Sci. 2020, 10(14), 4696; https://doi.org/10.3390/app10144696 - 08 Jul 2020
Cited by 20 | Viewed by 3674
Abstract
This paper presents a performance evaluation of the development of the instrumentation, communications and control systems of a two-tank process by using low-cost hardware and open source software. The hardware used for automating this process consists of embedded platforms (Arduino and Raspberry Pi) [...] Read more.
This paper presents a performance evaluation of the development of the instrumentation, communications and control systems of a two-tank process by using low-cost hardware and open source software. The hardware used for automating this process consists of embedded platforms (Arduino and Raspberry Pi) integrated into programmable logic controllers (PLCs), which are connected to a supervisory control and data acquisition (SCADA) system implemented with an open source Industrial Internet of Things (IIoT) platform. The main purpose of the proposed approach is to evaluate low-cost automation solutions (hardware and software) within the framework of modern industry requirements in order to determine whether these technologies could be enabling factors of IIoT. The proposed control strategy for regulating tank levels combines the classic PID algorithm and the fuzzy gain scheduling PID (FGS-PID) approach. Fault detection capabilities are also enabled for the system through a fault detection and diagnosis module (FDD) implemented with an extended Kalman filter (EKF). The distributed controller’s (DC) algorithms are embedded into the PLC’s processors in order to demonstrate the flexibility of the proposed system. Additionally, a remote human to machine interface (HMI) is deployed through a web client of the IIoT application. Experimental results show the proper operation of the overall system. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

24 pages, 4764 KiB  
Article
Visual Workflow Process Modeling and Simulation Approach Based on Non-Functional Properties of Resources
by Kawtar Ougaabal, Gregory Zacharewicz, Yves Ducq and Said Tazi
Appl. Sci. 2020, 10(13), 4664; https://doi.org/10.3390/app10134664 - 06 Jul 2020
Cited by 10 | Viewed by 3321
Abstract
With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in [...] Read more.
With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in which information systems, devices, and people must interact. It incorporates a number of fields including modeling and simulation (M&S). This paper focused on M&S of data workflow processes that are key steps to bridging the gap between business views and goals on the one side, and operational implementations on the other side. Simulation adds a dynamic view to static modeling; it increases understanding of the behavior of process mechanisms and the identification of weak areas in data flow. Several research projects have been focused on control flow and data flow, but less attention has been paid to resource characteristics. This work is based on the MDSEA approach and the eBPMN language, and proposes an approach that aims to distinguish the types of resources carrying out process tasks. Furthermore, it introduces a new composite resource made from the relationship between a user (human resource) and a task form (IT resource). Moreover, it proposes a resource aggregation based on process performance combination in order to run and display a global performance measurement of a process path. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

23 pages, 668 KiB  
Article
Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution
by José Luis Garrido-Labrador, Daniel Puente-Gabarri, José Miguel Ramírez-Sanz, David Ayala-Dulanto and Jesus Maudes
Appl. Sci. 2020, 10(13), 4606; https://doi.org/10.3390/app10134606 - 02 Jul 2020
Cited by 5 | Viewed by 3685
Abstract
The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with [...] Read more.
The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

20 pages, 2811 KiB  
Article
A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
by Raquel Redondo, Álvaro Herrero, Emilio Corchado and Javier Sedano
Appl. Sci. 2020, 10(12), 4355; https://doi.org/10.3390/app10124355 - 25 Jun 2020
Cited by 16 | Viewed by 3071
Abstract
In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them [...] Read more.
In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

29 pages, 6655 KiB  
Article
The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing
by Jonnro Erasmus, Irene Vanderfeesten, Konstantinos Traganos, Ruud Keulen and Paul Grefen
Appl. Sci. 2020, 10(12), 4145; https://doi.org/10.3390/app10124145 - 16 Jun 2020
Cited by 24 | Viewed by 4985
Abstract
Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety [...] Read more.
Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

14 pages, 1723 KiB  
Article
Dialogue Enhanced Extended Reality: Interactive System for the Operator 4.0
by Manex Serras, Laura García-Sardiña, Bruno Simões, Hugo Álvarez and Jon Arambarri
Appl. Sci. 2020, 10(11), 3960; https://doi.org/10.3390/app10113960 - 07 Jun 2020
Cited by 29 | Viewed by 3285
Abstract
The nature of industrial manufacturing processes and the continuous need to adapt production systems to new demands require tools to support workers during transitions to new processes. At the early stage of transitions, human error rate is often high and the impact in [...] Read more.
The nature of industrial manufacturing processes and the continuous need to adapt production systems to new demands require tools to support workers during transitions to new processes. At the early stage of transitions, human error rate is often high and the impact in quality and production loss can be significant. Over the past years, eXtended Reality (XR) technologies (such as virtual, augmented, immersive, and mixed reality) have become a popular approach to enhance operators’ capabilities in the Industry 4.0 paradigm. The purpose of this research is to explore the usability of dialogue-based XR enhancement to ease the cognitive burden associated with manufacturing tasks, through the augmentation of linked multi-modal information available to support operators. The proposed Interactive XR architecture, using the Spoken Dialogue Systems’ modular and user-centred architecture as a basis, was tested in two use case scenarios: the maintenance of a robotic gripper and as a shop-floor assistant for electric panel assembly. In both cases, we have confirmed a high user acceptance rate with an efficient knowledge communication and distribution even for operators without prior experience or with cognitive impairments, therefore demonstrating the suitability of the solution for assisting human workers in industrial manufacturing processes. The results endorse an initial validation of the Interactive XR architecture to achieve a multi-device and user-friendly experience to solve industrial processes, which is flexible enough to encompass multiple tasks. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

20 pages, 3674 KiB  
Article
Data Augmentation Method by Applying Color Perturbation of Inverse PSNR and Geometric Transformations for Object Recognition Based on Deep Learning
by Eun Kyeong Kim, Hansoo Lee, Jin Yong Kim and Sungshin Kim
Appl. Sci. 2020, 10(11), 3755; https://doi.org/10.3390/app10113755 - 28 May 2020
Cited by 15 | Viewed by 4830
Abstract
Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target [...] Read more.
Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target items that are to be classified are generally excluded from existing databases, and the manual collection of images poses certain limitations. Therefore, to overcome the data deficiency that is present in many domains including manufacturing, we propose a method of generating new training images via image pre-processing steps, background elimination, target extraction while maintaining the ratio of the object size in the original image, color perturbation considering the predefined similarity between the original and generated images, geometric transformations, and transfer learning. Specifically, to demonstrate color perturbation and geometric transformations, we compare and analyze the experiments of each color space and each geometric transformation. The experimental results show that the proposed method can effectively augment the original data, correctly classify similar items, and improve the image classification accuracy. In addition, it also demonstrates that the effective data augmentation method is crucial when the amount of training data is small. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Graphical abstract

23 pages, 3454 KiB  
Article
Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
by Daniel Mejia-Parra, Ander Arbelaiz, Oscar Ruiz-Salguero, Juan Lalinde-Pulido, Aitor Moreno and Jorge Posada
Appl. Sci. 2020, 10(9), 3281; https://doi.org/10.3390/app10093281 - 08 May 2020
Cited by 2 | Viewed by 2585
Abstract
In flexible manufacturing systems, fast feedback from simulation solutions is required for effective tool path planning and parameter optimization. In the particular sub-domain of laser heating/cutting of thin rectangular plates, current state-of-the-art methods include frequency-domain (spectral) analytic solutions that greatly reduce the required [...] Read more.
In flexible manufacturing systems, fast feedback from simulation solutions is required for effective tool path planning and parameter optimization. In the particular sub-domain of laser heating/cutting of thin rectangular plates, current state-of-the-art methods include frequency-domain (spectral) analytic solutions that greatly reduce the required computational time in comparison to industry standard finite element based approaches. However, these spectral solutions have not been presented previously in terms of Fourier methods and Fast Fourier Transform (FFT) implementations. This manuscript presents four different schemes that translate the problem of laser heating of rectangular plates into equivalent FFT problems. The presented schemes make use of the FFT algorithm to reduce the computational time complexity of the problem from O ( M 2 N 2 ) to O ( M N log ( M N ) ) (with M × N being the discretization size of the plate). The test results show that the implemented schemes outperform previous non-FFT approaches both in CPU and GPU hardware, resulting in 100 × faster runs. Future work addresses thermal/stress analysis, non-rectangular geometries and non-linear interactions (such as material melting/ablation, convection and radiation heat transfer). Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

22 pages, 1290 KiB  
Article
An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
by Shengkai Chen, Shuliang Fang and Renzhong Tang
Appl. Sci. 2020, 10(7), 2491; https://doi.org/10.3390/app10072491 - 05 Apr 2020
Cited by 17 | Viewed by 2573
Abstract
The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type [...] Read more.
The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

14 pages, 3263 KiB  
Article
Predictive Maintenance on the Machining Process and Machine Tool
by Alberto Jimenez-Cortadi, Itziar Irigoien, Fernando Boto, Basilio Sierra and German Rodriguez
Appl. Sci. 2020, 10(1), 224; https://doi.org/10.3390/app10010224 - 27 Dec 2019
Cited by 39 | Viewed by 6066
Abstract
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main [...] Read more.
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

11 pages, 1097 KiB  
Article
Knowledge Absorption Capacity as a Factor for Increasing Logistics 4.0 Maturity
by Agnieszka Stachowiak, Michał Adamczak, Lukasz Hadas, Roman Domański and Piotr Cyplik
Appl. Sci. 2019, 9(24), 5365; https://doi.org/10.3390/app9245365 - 09 Dec 2019
Cited by 10 | Viewed by 3150
Abstract
This research strives to show the importance of knowledge absorptive capacity as one of the most important determinants of successful implementation of contemporary solutions and, consequently, development of a company. In the approach presented, the development leads to excellence and is expressed with [...] Read more.
This research strives to show the importance of knowledge absorptive capacity as one of the most important determinants of successful implementation of contemporary solutions and, consequently, development of a company. In the approach presented, the development leads to excellence and is expressed with subsequent maturity levels. The research is focused on identification of the level of absorption of knowledge of contemporary solutions in logistics, grouped in a concept of Logistics 4.0, and how that upgrades the organizational maturity of a company. The research was conducted with CAWI (Computer-Assisted Web Interview), including three questions and a basic query on experts’ qualifications. The general conclusion from the research was that to reach a higher level of maturity, a higher level of knowledge absorption is required. However, searching for differences in absorption of solutions within physical flows, information flows and managerial methods seem to be an interesting issue and promising field for further research. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

11 pages, 2611 KiB  
Article
Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net
by Chao-Nan Chen, Tung-Kuan Liu and Yenming J. Chen
Appl. Sci. 2019, 9(23), 5066; https://doi.org/10.3390/app9235066 - 24 Nov 2019
Cited by 9 | Viewed by 2842
Abstract
This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data. As part of Industry 4.0, a human operator possesses various characteristics, but cannot be consistently understood as well as a machine. Thus, human-machine [...] Read more.
This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data. As part of Industry 4.0, a human operator possesses various characteristics, but cannot be consistently understood as well as a machine. Thus, human-machine interaction plays an important role. This study focuses on incumbent challenges on the basis of estimated mental states. Given the operation logs from data recording hardware, a Hidden Markov model on top of a human cognitive model was trained to capture a production line worker’s sequential faults. Our study found that retaining workers’ attention is insufficient and tracking the state of perception is key to accomplishing production tasks. A safe workflow policy requires attention and perception. Accordingly, our proposed Petri Net enhances operation safety and improves production efficiency. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

18 pages, 3013 KiB  
Article
Flexible Framework to Model Industry 4.0 Processes for Virtual Simulators
by Kiara Ottogalli, Daniel Rosquete, Aiert Amundarain, Iker Aguinaga and Diego Borro
Appl. Sci. 2019, 9(23), 4983; https://doi.org/10.3390/app9234983 - 20 Nov 2019
Cited by 24 | Viewed by 3707
Abstract
Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by [...] Read more.
Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by the worker and other elements involved, such as robots and machinery. This work proposes a framework to model industrial processes for VR and AR simulators. The desk method was used to review previous research and extract the most important features of current approaches. Novel features include interaction among human workers and a variety of automation systems, such as collaborative robots, a broader set of tasks (including assembly and disassembly of components), flexibility of modeling industrial processes for different domains and purposes, a clear separation of process definition and simulator, and independence of specific programming languages or technologies. Three industrial scenarios modeled with this framework are presented: an aircraft assembly scenario, a guidance tool for high-voltage cell security, and an application for the training of machine-tool usage. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

13 pages, 3842 KiB  
Article
Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0
by Le Vu Tran, Bao Huy Huynh and Humza Akhtar
Appl. Sci. 2019, 9(22), 4815; https://doi.org/10.3390/app9224815 - 11 Nov 2019
Cited by 15 | Viewed by 5228
Abstract
Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry and scheduling of MRO processes is significantly different from conventional manufacturing processes. In this study, we adopted a swarm intelligent algorithm, Ant Colony Optimization (ACO), to solve the scheduling optimization [...] Read more.
Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry and scheduling of MRO processes is significantly different from conventional manufacturing processes. In this study, we adopted a swarm intelligent algorithm, Ant Colony Optimization (ACO), to solve the scheduling optimization of MRO processes with two business objectives: minimizing the total scheduling time (make-span) and total tardiness of all jobs. The algorithm also has the dynamic scheduling capability which can help the scheduler to cope with the changes in the shop floor which frequently occur in the MRO processes. Results from the developed algorithm have shown its better solution in comparison to commercial scheduling software. The dependency of the algorithm’s performance on tuning parameters has been investigated and an approach to shorten the convergence time of the algorithm is emerging. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research

31 pages, 2520 KiB  
Review
A Review of Industry 4.0 Manufacturing Process Security Risks
by Jaco Prinsloo, Saurabh Sinha and Basie von Solms
Appl. Sci. 2019, 9(23), 5105; https://doi.org/10.3390/app9235105 - 26 Nov 2019
Cited by 78 | Viewed by 12325
Abstract
The advent of three-dimensional (3D) printing has found a unique and prominent role in Industry 4.0 and is rapidly gaining popularity in the manufacturing industry. 3D printing offers many advantages over conventional manufacturing methods, making it an attractive alternative that is more cost-effective [...] Read more.
The advent of three-dimensional (3D) printing has found a unique and prominent role in Industry 4.0 and is rapidly gaining popularity in the manufacturing industry. 3D printing offers many advantages over conventional manufacturing methods, making it an attractive alternative that is more cost-effective and efficient than conventional manufacturing methods. With the Internet of Things (IoT) at the heart of this new movement, control over manufacturing methods now enters the cyber domain, offering endless possibilities in manufacturing automation and optimization. However, as disruptive and innovative as this may seem, there is grave concern about the cyber-security risks involved. These security aspects are often overlooked, particularly by promising new start-ups and parties that are not too familiar with the risks involved in not having proper cyber-security measures in place. This paper explores some of the cyber-security risks involved in the bridge between industrial manufacturing and Industry 4.0, as well as the associated countermeasures already deployed or currently under development. These aspects are then contextualized in terms of Industry 4.0 in order to serve as a basis for and assist with future development in this field. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-19
Back to TopTop