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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

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

Deadline for manuscript submissions: closed (15 August 2019) | Viewed by 105462
Related Special Issue: New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume III

Printed Edition Available!
A printed edition of this Special Issue is available here.

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,

Today, industrial factories are 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, fogor cloud computing technologies. Manufacturing processes 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.

In this line, the manufacturing process models (e.g. thermal, vibration, deformation, etc.) 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 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.-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 (24 papers)

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Editorial

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3 pages, 207 KiB  
Editorial
Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes
by Luis Norberto López de Lacalle and Jorge Posada
Appl. Sci. 2019, 9(20), 4323; https://doi.org/10.3390/app9204323 - 14 Oct 2019
Cited by 6 | Viewed by 2014
Abstract
The new advances of IIOT (Industrial Internet of Things), together with the progress in visual computing technologies, are being addressed by the research community with interesting approaches and results in the 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)

Research

Jump to: Editorial, Review

18 pages, 14539 KiB  
Article
Hybrid Edge–Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
by Ruben Merino, Iñigo Bediaga, Alexander Iglesias and Jokin Munoa
Appl. Sci. 2019, 9(20), 4283; https://doi.org/10.3390/app9204283 - 12 Oct 2019
Cited by 13 | Viewed by 2804
Abstract
The contact profile of a train wheel has a key role in its operation performance. Rolling smoothly and with reduced resistance results in an increase in the efficiency and safety of rail transport. The original shape and dimensions of the profile of the [...] Read more.
The contact profile of a train wheel has a key role in its operation performance. Rolling smoothly and with reduced resistance results in an increase in the efficiency and safety of rail transport. The original shape and dimensions of the profile of the wheel are altered under operation of the train, especially due to braking events and the presence of external objects between the wheel and the railway. With the purpose of recovering the optimum contact profile, train wheels are periodically machined using special lathes. This repair operation is particularly critical in freight trains, which are only reshaped a few times throughout their service life and, therefore, high depths of cut are required to recover the wheel in a productive way. As the presence of chatter vibrations limits the productivity of these operations, a hybrid edge–cloud computing approach has been developed for chatter vibration suppression. An expert system based on automatic chatter detection and suppression has been developed in the edge. The expert system is based on continuous real-time vibration monitoring and combines continuous spindle speed variation (CSSV) and cutting speed reduction to suppress chatter. Cloud computing is used to extract wheel profile machining fingerprints and obtain insights from multiple aggregated machined wheels. An industrial implementation of the system is described in the present work. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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20 pages, 5574 KiB  
Article
The PLC as a Smart Service in Industry 4.0 Production Systems
by Reinhard Langmann and Michael Stiller
Appl. Sci. 2019, 9(18), 3815; https://doi.org/10.3390/app9183815 - 11 Sep 2019
Cited by 45 | Viewed by 9114
Abstract
Industrial controls, and in particular, Programmable Logic controllers (PLC) currently form an important technological basis for the automation of industrial processes. Even in the age of industry 4.0 and industrial internet, it can be assumed that these controllers will continue to be required [...] Read more.
Industrial controls, and in particular, Programmable Logic controllers (PLC) currently form an important technological basis for the automation of industrial processes. Even in the age of industry 4.0 and industrial internet, it can be assumed that these controllers will continue to be required to a considerable extent for the production of tomorrow. However, the controllers must fulfill a range of additional requirements, resulting from the new production conditions. Thereby, the introduction of the service paradigm plays an important role. This paper presents the concept of smart industrial control services (SICS) as a new type of a PLC. As a distributed service-oriented control system in an IP network, a SICS controller can replace the traditional PLC for applications with uncritical timing in terms of Industry 4.0. The SICS are programmed as usual in industry, according to the standard IEC 61131-3, and run in a SICS runtime on a server or in a cloud. The term Smart Service is introduced and the uses of SICS as a smart service, including a clearing system for the creation of new business models based on control as a service, are described. As a result, two different SICS prototype implementations are described and two application examples from manufacturing automation, as well as the evaluation of the real-time features and the engineering of a SICS controller, are discussed in the paper. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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26 pages, 889 KiB  
Article
Harnessing the Full Potential of Industrial Demand-Side Flexibility: An End-to-End Approach Connecting Machines with Markets through Service-Oriented IT Platforms
by Martin Roesch, Dennis Bauer, Leon Haupt, Robert Keller, Thomas Bauernhansl, Gilbert Fridgen, Gunther Reinhart and Alexander Sauer
Appl. Sci. 2019, 9(18), 3796; https://doi.org/10.3390/app9183796 - 10 Sep 2019
Cited by 27 | Viewed by 4726
Abstract
The growing share of renewable energy generation based on fluctuating wind and solar energy sources is increasingly challenging in terms of power grid stability. Industrial demand-side response presents a promising way to balance energy supply and consumption. For this, energy demand is flexibly [...] Read more.
The growing share of renewable energy generation based on fluctuating wind and solar energy sources is increasingly challenging in terms of power grid stability. Industrial demand-side response presents a promising way to balance energy supply and consumption. For this, energy demand is flexibly adapted based on external incentives. Thus, companies can economically benefit and at the same time contribute to reducing greenhouse gas emissions. However, there are currently some major obstacles that impede industrial companies from taking part in the energy markets. A broad specification analysis systematically dismantles the existing barriers. On this foundation, a new end-to-end ecosystem of an energy synchronization platform is introduced. It consists of a business-individual company-side platform, where suitable services for energy-oriented manufacturing are offered. In addition, one market-side platform is established as a mediating service broker, which connects the companies to, e.g., third party service providers, energy suppliers, aggregators, and energy markets. The ecosystems aim at preventing vendor lock-in and providing a flexible solution, relying on open standards and offering an integrated solution through an end-to-end energy flexibility data model. In this article, the resulting functionalities are discussed and the remaining deficits outlined. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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13 pages, 2774 KiB  
Article
YOLOv3-Lite: A Lightweight Crack Detection Network for Aircraft Structure Based on Depthwise Separable Convolutions
by Yadan Li, Zhenqi Han, Haoyu Xu, Lizhuang Liu, Xiaoqiang Li and Keke Zhang
Appl. Sci. 2019, 9(18), 3781; https://doi.org/10.3390/app9183781 - 10 Sep 2019
Cited by 72 | Viewed by 6990
Abstract
Due to the high proportion of aircraft faults caused by cracks in aircraft structures, crack inspection in aircraft structures has long played an important role in the aviation industry. The existing approaches, however, are time-consuming or have poor accuracy, given the complex background [...] Read more.
Due to the high proportion of aircraft faults caused by cracks in aircraft structures, crack inspection in aircraft structures has long played an important role in the aviation industry. The existing approaches, however, are time-consuming or have poor accuracy, given the complex background of aircraft structure images. In order to solve these problems, we propose the YOLOv3-Lite method, which combines depthwise separable convolution, feature pyramids, and YOLOv3. Depthwise separable convolution is employed to design the backbone network for reducing parameters and for extracting crack features effectively. Then, the feature pyramid joins together low-resolution, semantically strong features at a high-resolution for obtaining rich semantics. Finally, YOLOv3 is used for the bounding box regression. YOLOv3-Lite is a fast and accurate crack detection method, which can be used on aircraft structure such as fuselage or engine blades. The result shows that, with almost no loss of detection accuracy, the speed of YOLOv3-Lite is 50% more than that of YOLOv3. It can be concluded that YOLOv3-Lite can reach state-of-the-art performance. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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22 pages, 7435 KiB  
Article
Pushing Digital Automation of Configure-to-Order Services in Small and Medium Enterprises of the Construction Equipment Industry: A Design Science Research Approach
by Christoph Paul Schimanski, Gabriele Pasetti Monizza, Carmen Marcher and Dominik T. Matt
Appl. Sci. 2019, 9(18), 3780; https://doi.org/10.3390/app9183780 - 09 Sep 2019
Cited by 28 | Viewed by 4770
Abstract
In order to efficiently transform business processes (such as product design, product engineering, production, logistics, sales, deliveries, etc.) into digitally automated processes, new concepts have been introduced in both the manufacturing and construction industries. Under the term Industry 4.0, promising possibilities for high-performance [...] Read more.
In order to efficiently transform business processes (such as product design, product engineering, production, logistics, sales, deliveries, etc.) into digitally automated processes, new concepts have been introduced in both the manufacturing and construction industries. Under the term Industry 4.0, promising possibilities for high-performance production processes are emerging based on e.g., digital twins and cyber-physical systems. However, the construction industry lags behind in adapting these ideas, and is still facing severe productivity deficits. This paper addresses these deficits by assessing the hypothesis of Building Information Modeling—as the digital twinning methodology in construction—representing a key driver for digital automation and thus enabling more productive processes. To this end, we apply a design science research approach to develop artefacts using computational methods for the automation of business processes in a configure-to-order industry partner. The evaluation is done in the context of a pilot project together with this industry partner. The findings obtained in the pilot project revealed time savings in the phases of bid estimation and work preparation. Based on the findings, the applicability and utility of the suggested approach are discussed and allow for the conclusion that Building Information Model data can usefully streamline and automate many processes at the interface between design and production, if structured and preprocessed purposefully. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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21 pages, 578 KiB  
Article
Marketing Innovations in Industry 4.0 and Their Impacts on Current Enterprises
by Otakar Ungerman and Jaroslava Dědková
Appl. Sci. 2019, 9(18), 3685; https://doi.org/10.3390/app9183685 - 05 Sep 2019
Cited by 15 | Viewed by 5544
Abstract
This paper discussed the marketing innovations associated with Industry 4.0 and the effects that these innovative approaches cause. The main aim of the research was to discover the relationship between marketing innovations and their effects. Knowledge of this relationship can be used for [...] Read more.
This paper discussed the marketing innovations associated with Industry 4.0 and the effects that these innovative approaches cause. The main aim of the research was to discover the relationship between marketing innovations and their effects. Knowledge of this relationship can be used for the strategic planning of industrial companies in practice. The research methodology consisted of pilot research followed by primary research in industrial enterprises. The data were evaluated by descriptive statistics, statistical hypothesis, and correlation analysis. Through the research, the authors identified the importance of 17 innovative marketing tools and the strength of the use of 11 effects resulting from the implementation of these tools. The authors identified the relationships between tools and their implications in Industry 4.0 where a correlation was demonstrated. A list of 11 strategic objectives was created and, subsequently, a specific marketing mix proposal for each objective consisting of innovative marketing tools was as well. The results of this work enable enterprises involved in Industry 4.0 to better plan. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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16 pages, 17066 KiB  
Article
Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
by Huanhuan Zhang, Jinxiu Ma, Junfeng Jing and Pengfei Li
Appl. Sci. 2019, 9(17), 3506; https://doi.org/10.3390/app9173506 - 26 Aug 2019
Cited by 26 | Viewed by 4891
Abstract
In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric [...] Read more.
In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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11 pages, 5619 KiB  
Article
The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
by Zekui Lv, Zhikun Su, Dong Zhang, Lingyu Gao, Zhiming Yang, Fengzhou Fang, Haitao Zhang and Xinghua Li
Appl. Sci. 2019, 9(17), 3485; https://doi.org/10.3390/app9173485 - 23 Aug 2019
Cited by 2 | Viewed by 2511
Abstract
The elliptical paraboloid array plays an important role in precision measurement, astronomical telescopes, and communication systems. The calibration of the vertex distance of elliptical paraboloids is of great significance to precise 2D displacement measurement. However, there are some difficulties in determining the vertex [...] Read more.
The elliptical paraboloid array plays an important role in precision measurement, astronomical telescopes, and communication systems. The calibration of the vertex distance of elliptical paraboloids is of great significance to precise 2D displacement measurement. However, there are some difficulties in determining the vertex position with contact measurement. In this study, an elliptical paraboloid array and an optical slope sensor for displacement measurement were designed and analyzed. Meanwhile, considering the geometrical relationship and relative angle between elliptical paraboloids, a non-contact self-calibration method for the vertex distance of the elliptical paraboloid array was proposed. The proposed self-calibration method was verified by a series of experiments with a high repeatability, within 3   μ m in the X direction and within 1   μ m in the Y direction. Through calibration, the displacement measurement system error was reduced from 100   μ m to 3   μ m . The self-calibration method of the elliptical paraboloid array has great potential in the displacement measurement field, with a simple principle and high precision. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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17 pages, 3942 KiB  
Article
A Generic Automated Surface Defect Detection Based on a Bilinear Model
by Fei Zhou, Guihua Liu, Feng Xu and Hao Deng
Appl. Sci. 2019, 9(15), 3159; https://doi.org/10.3390/app9153159 - 03 Aug 2019
Cited by 34 | Viewed by 3887
Abstract
Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, [...] Read more.
Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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22 pages, 2646 KiB  
Article
Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning
by Mingxiong Zhao, Han Wang, Jin Guo, Di Liu, Cheng Xie, Qing Liu and Zhibo Cheng
Appl. Sci. 2019, 9(13), 2720; https://doi.org/10.3390/app9132720 - 05 Jul 2019
Cited by 31 | Viewed by 6515
Abstract
The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this [...] Read more.
The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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10 pages, 1506 KiB  
Article
Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution
by Hongyang Li, Lizhuang Liu, Zhenqi Han and Dan Zhao
Appl. Sci. 2019, 9(13), 2684; https://doi.org/10.3390/app9132684 - 01 Jul 2019
Cited by 1 | Viewed by 2723
Abstract
Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is [...] Read more.
Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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10 pages, 565 KiB  
Article
Technology Portfolio and Role of Public Research Institutions in Industry 4.0: A Case of South Korea
by SungUk Lim and Junmo Kim
Appl. Sci. 2019, 9(13), 2632; https://doi.org/10.3390/app9132632 - 28 Jun 2019
Cited by 4 | Viewed by 2698
Abstract
The 4th industrial revolution has been a hot topic in various societies for several overlapping reasons. It may be a huge wave for researchers to navigate through. In this context, research institutions are not different from major industrial sectors, in that both consider [...] Read more.
The 4th industrial revolution has been a hot topic in various societies for several overlapping reasons. It may be a huge wave for researchers to navigate through. In this context, research institutions are not different from major industrial sectors, in that both consider the 4th revolution a major turning point as well as a threat. Today’s industries and research institutions are knowledge-intensive in nature. Consequently, their potential for survival depends on scientific and technological aspects as well as their organizational dimension. This study analyzes 25 major public research institutions in South Korea, located in the DaeDuk area, based on their technological capability for organizational and expert evaluation. It also proposes a matching scheme between research institutions and research topics related to the 4th industrial revolution. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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17 pages, 484 KiB  
Article
Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems
by Ping Liu, Qiang Zhang and Jürgen Pannek
Appl. Sci. 2019, 9(11), 2249; https://doi.org/10.3390/app9112249 - 31 May 2019
Cited by 13 | Viewed by 3162
Abstract
With the development of industrial manufacture in the context of Industry 4.0, various advanced technologies have been designed, such as reconfigurable machine tools (RMT). However, the potential of the latter still needs to be developed. In this paper, the integration of RMTs was [...] Read more.
With the development of industrial manufacture in the context of Industry 4.0, various advanced technologies have been designed, such as reconfigurable machine tools (RMT). However, the potential of the latter still needs to be developed. In this paper, the integration of RMTs was investigated in the capacity adjustment of job shop manufacturing systems, which offer high flexibility to produce a variety of products with small lot sizes. In order to assist manufacturers in dealing with demand fluctuations and ensure the work-in-process (WIP) of each workstation is on a predefined level, an operator-based robust right coprime factorization (RRCF) approach is proposed to improve the capacity adjustment process. Moreover, numerical simulation results of a four-workstation three-product job shop system are presented, where the classical proportional–integral–derivative (PID) control method is considered as a benchmark to evaluate the effectiveness of RRCF in the simulation. The simulation results present the practical stability and robustness of these two control systems for various reconfiguration and transportation delays and disturbances. This indicates that the proposed capacity control approach by integrating RMTs with RRCF is effective in dealing with bottlenecks and volatile customer demands. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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12 pages, 6201 KiB  
Article
An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones
by Jiange Liu, Tao Feng, Xia Fang, Sisi Huang and Jie Wang
Appl. Sci. 2019, 9(11), 2185; https://doi.org/10.3390/app9112185 - 28 May 2019
Cited by 4 | Viewed by 2452
Abstract
Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is [...] Read more.
Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is necessary to develop an automatic defect detection process. In this work, an elaborated vision system for the defect inspection of micro-armatures used in smartphones was developed. It consists of two parts, the front-end module and the deep convolution neural networks (DCNNs) module, which are responsible for different areas. The front-end module runs first and the DCNNs module will not run if the output of the front-end module is negative. To verify the application of this system, an apparatus consisting of an objective table, control panel, and a camera connected to a Personal Computer (PC) was used to simulate an industrial position of production. The results indicate that the developed vision system is capable of defect detection of micro-armatures. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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16 pages, 3426 KiB  
Article
An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0
by Justyna Patalas-Maliszewska and Sławomir Kłos
Appl. Sci. 2019, 9(9), 1848; https://doi.org/10.3390/app9091848 - 05 May 2019
Cited by 15 | Viewed by 2826
Abstract
(1) Background: In recent years, many studies regarding the issues of improving the management and effectiveness of the maintenance department of manufacturing companies, in the context Industry 4.0, have been published. This makes it necessary to establish a research gap in the approach [...] Read more.
(1) Background: In recent years, many studies regarding the issues of improving the management and effectiveness of the maintenance department of manufacturing companies, in the context Industry 4.0, have been published. This makes it necessary to establish a research gap in the approach to obtaining support in realising management tasks in the maintenance area in the selection of appropriate employees to perform the given activities. (2) Methods: This article uses literature studies and empirical research results from manufacturing companies, in order to determine the approach in supporting the selection of maintenance experts. In the approach, the method used—which is based on rules should there be future any formalisation of the data—is also the Fuzzy Analytic Hierarchy Process (FAHP), which analyses the importance of a given competence, within a manufacturing resource, to undertake repairs. (3) Results: The innovative approach towards the selection of expert workers in a maintenance department is created, in part, in the form of an implemented web-application. The novelty of the “maintenance expert selection map", so-called, is the provision of formal procedures for describing the competence of each maintenance worker and defining the best “state of nature”. (4) Conclusions: In the research that is presented here, the practicality for maintenance managers in the “maintenance expert selection map" was established. This map describes the competence of workers for selecting them for repair work within a given manufacturing resource; the scope of employee training was also determined in this research. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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14 pages, 397 KiB  
Article
Implementation of R&D Results and Industry 4.0 Influenced by Selected Macroeconomic Indicators
by Jena Švarcová, Tomáš Urbánek, Lucie Povolná and Eliška Sobotková
Appl. Sci. 2019, 9(9), 1846; https://doi.org/10.3390/app9091846 - 05 May 2019
Cited by 12 | Viewed by 2886
Abstract
Successful timing of INDUSTRY 4.0 projects in businesses can be disrupted by the coming of a recession. The authors assume a close link between INDUSTRY 4.0 and research and development (R&D) projects. R&D projects are statistically internationally monitored and have a significant impact [...] Read more.
Successful timing of INDUSTRY 4.0 projects in businesses can be disrupted by the coming of a recession. The authors assume a close link between INDUSTRY 4.0 and research and development (R&D) projects. R&D projects are statistically internationally monitored and have a significant impact on European Union economic policies. This article explores the impact of the two economic recessions in 2009 and 2012–2013 on the number of R&D entities and human resources involved in R&D in the Czech Republic. The method of multivariate statistics with dummy variables was used. Research has shown that different sectors (business sector, government sector, higher education sector, and non-profit sector) show a different development of the number of R&D entities in times of economic crisis. The research findings indicate that current European Union grant support, tax relief, and other specific factors appear to be more important for the development of R&D projects in the Czech Republic than the effects of economic recession. In terms of longer time horizons, however, the effects of the business cycle cannot be ignored. In order to predict economic development, enterprises and other subjects can use leading macroeconomic indicators. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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24 pages, 2929 KiB  
Article
Industrial Cyber-Physical System Evolution Detection and Alert Generation
by Aitziber Iglesias, Goiuria Sagardui and Cristobal Arellano
Appl. Sci. 2019, 9(8), 1586; https://doi.org/10.3390/app9081586 - 17 Apr 2019
Cited by 8 | Viewed by 2674
Abstract
Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, [...] Read more.
Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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17 pages, 1610 KiB  
Article
A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes
by Yi-Chung Chen, Kuo-Cheng Ting, Yo-Ming Chen, Don-Lin Yang, Hsi-Min Chen and Josh Jia-Ching Ying
Appl. Sci. 2019, 9(8), 1549; https://doi.org/10.3390/app9081549 - 14 Apr 2019
Cited by 6 | Viewed by 2589
Abstract
Since the concept of Industry 4.0 emerged, an increasing number of major manufacturers have incorporated relevant technologies to monitor machinery and schedule processes so as to increase yield and optimize production. However, most machinery monitoring technologies are far too expensive for small- and [...] Read more.
Since the concept of Industry 4.0 emerged, an increasing number of major manufacturers have incorporated relevant technologies to monitor machinery and schedule processes so as to increase yield and optimize production. However, most machinery monitoring technologies are far too expensive for small- and medium-sized enterprises. Furthermore, the production processes at small- and medium-sized enterprises are simpler and can thus be optimized without excessively complex scheduling systems. This study therefore proposed the use of cheaper add-on sensors for monitoring machinery and integrated them with an algorithm that can more swiftly produce results that meet multiple objectives. The proposed algorithm is meant to extend the capabilities of small- and medium-sized enterprises in monitoring machinery and scheduling processes, thereby enabling them to contend on an equal footing with larger competitors. Finally, we performed an experiment at an actual spring enterprise to demonstrate the validity of the proposed algorithm. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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15 pages, 7766 KiB  
Article
Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
by Liyong Ma, Wei Xie and Yong Zhang
Appl. Sci. 2019, 9(6), 1085; https://doi.org/10.3390/app9061085 - 14 Mar 2019
Cited by 51 | Viewed by 5156
Abstract
To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell [...] Read more.
To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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21 pages, 4434 KiB  
Article
In-Line Dimensional Inspection of Warm-Die Forged Revolution Workpieces Using 3D Mesh Reconstruction
by Daniel Mejia-Parra, Jairo R. Sánchez, Oscar Ruiz-Salguero, Marcos Alonso, Alberto Izaguirre, Erik Gil, Jorge Palomar and Jorge Posada
Appl. Sci. 2019, 9(6), 1069; https://doi.org/10.3390/app9061069 - 14 Mar 2019
Cited by 19 | Viewed by 3971
Abstract
Industrial dimensional assessment presents instances in which early control is exerted among “warm” (approx. 600 ∘C) pieces. Early control saves resources, as defective processes are timely stopped and corrected. Existing literature is devoid of dimensional assessment on warm workpieces. In response to [...] Read more.
Industrial dimensional assessment presents instances in which early control is exerted among “warm” (approx. 600 ∘C) pieces. Early control saves resources, as defective processes are timely stopped and corrected. Existing literature is devoid of dimensional assessment on warm workpieces. In response to this absence, this manuscript presents the implementation and results of an optical system which performs in-line dimensional inspection of revolution warm workpieces singled out from the (forming) process. Our system can automatically measure, in less than 60 s, the circular runout of warm revolution workpieces. Such a delay would be 20 times longer if cool-downs were required. Off-line comparison of the runout of T-temperature workpieces (27 ∘C ≤ T ≤ 560 ∘C) shows a maximum difference of 0.1 mm with respect to standard CMM (Coordinate Measurement Machine) runout of cold workpieces (27 ∘C), for workpieces as long as 160 mm. Such a difference is acceptable for the forging process in which the system is deployed. The test results show no correlation between the temperature and the runout of the workpiece at such level of uncertainty. A prior-to-operation Analysis of Variance (ANOVA) test validates the repeatability and reproducibility (R&R) of our measurement system. In-line assessment of warm workpieces fills a gap in manufacturing processes where early detection of dimensional misfits compensates for the precision loss of the vision system. The integrated in-line system reduces the number of defective workpieces by 95 % . Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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25 pages, 4622 KiB  
Article
Cutting Insert and Parameter Optimization for Turning Based on Artificial Neural Networks and a Genetic Algorithm
by Bolivar Solarte-Pardo, Diego Hidalgo and Syh-Shiuh Yeh
Appl. Sci. 2019, 9(3), 479; https://doi.org/10.3390/app9030479 - 30 Jan 2019
Cited by 15 | Viewed by 3833
Abstract
The objective of this present study is to develop a system to optimize cutting insert selection and cutting parameters. The proposed approach addresses turning processes that use technical information from a tool supplier. The proposed system is based on artificial neural networks and [...] Read more.
The objective of this present study is to develop a system to optimize cutting insert selection and cutting parameters. The proposed approach addresses turning processes that use technical information from a tool supplier. The proposed system is based on artificial neural networks and a genetic algorithm, which define the modeling and optimization stages, respectively. For the modeling stage, two artificial neural networks are implemented to evaluate the feed rate and cutting velocity parameters. These models are defined as functions of insert features and working conditions. For the optimization problem, a genetic algorithm is implemented to search an optimal tool insert. This heuristic algorithm is evaluated using a custom objective function, which assesses the machining performance based on the given working specifications, such as the lowest power consumption, the shortest machining time or an acceptable surface roughness. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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Graphical abstract

Review

Jump to: Editorial, Research

12 pages, 580 KiB  
Review
Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives
by Jon Kepa Gerrikagoitia, Gorka Unamuno, Elena Urkia and Ainhoa Serna
Appl. Sci. 2019, 9(14), 2934; https://doi.org/10.3390/app9142934 - 23 Jul 2019
Cited by 71 | Viewed by 11485
Abstract
The fourth industrial revolution is characterized by the introduction of the Internet of things (IoT) and Internet of Services (IoS) concepts into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The main driver is technology, as Industry 4.0 is [...] Read more.
The fourth industrial revolution is characterized by the introduction of the Internet of things (IoT) and Internet of Services (IoS) concepts into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The main driver is technology, as Industry 4.0 is a collective term for technologies and concepts of value chain organization. Digital manufacturing platforms play an increasing role in dealing with competitive pressures and incorporating new technologies, applications, and services. Motivated by the difficulties to understand and adopt Industry 4.0 and the momentum that the topic has currently, this paper reviews the concepts and approaches related to digital manufacturing platforms from different perspectives: IoT platforms, digital manufacturing platforms, digital platforms as ecosystems, digital platforms from research and development perspective, and digital platform from industrial equipment suppliers. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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23 pages, 738 KiB  
Review
A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise
by Phillip M. LaCasse, Wilkistar Otieno and Francisco P. Maturana
Appl. Sci. 2019, 9(5), 843; https://doi.org/10.3390/app9050843 - 27 Feb 2019
Cited by 13 | Viewed by 3802
Abstract
The broad context of this literature review is the connected manufacturing enterprise, characterized by a data environment such that the size, structure and variety of information strain the capability of traditional software and database tools to effectively capture, store, manage and analyze it. [...] Read more.
The broad context of this literature review is the connected manufacturing enterprise, characterized by a data environment such that the size, structure and variety of information strain the capability of traditional software and database tools to effectively capture, store, manage and analyze it. This paper surveys and discusses representative examples of existing research into approaches for feature set reduction in the big data environment, focusing on three contexts: general industrial applications; specific industrial applications such as fault detection or fault prediction; and data reduction. The conclusion from this review is that there is room for research into frameworks or approaches to feature filtration and prioritization, specifically with respect to providing quantitative or qualitative information about the individual features in the dataset that can be used to rank features against each other. A byproduct of this gap is a tendency for analysts not to holistically generalize results beyond the specific problem of interest, and, related, for manufacturers to possess only limited knowledge of the relative value of smart manufacturing data collected. Full article
(This article belongs to the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes)
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