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4 pages, 194 KiB

Open AccessEditorial

Special Issue on Advanced Design and Manufacturing in Industry 4.0

by Giuseppe Marannano and Antonio Giallanza

Appl. Sci. 2023, 13(2), 767; https://doi.org/10.3390/app13020767 - 5 Jan 2023

Viewed by 1552

Abstract

Industry 4 [...] Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

Research

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11 pages, 1536 KiB

Open AccessArticle

Development of an Algorithm for Determining Defects in Cast-in-Place Piles Based on the Data Analysis of Low Strain Integrity Testing

by Natalia Koteleva and Elizaveta Loseva

Appl. Sci. 2022, 12(20), 10636; https://doi.org/10.3390/app122010636 - 21 Oct 2022

Cited by 10 | Viewed by 1836

Abstract

Low strain integrity testing for pile quality control, based on the analysis of elastic waves, is one of the most common methods, due to its high efficiency. However, it also has a number of limitations that should be taken into account during pile [...] Read more.

Low strain integrity testing for pile quality control, based on the analysis of elastic waves, is one of the most common methods, due to its high efficiency. However, it also has a number of limitations that should be taken into account during pile testing. For additional study of the method and its effectiveness, an experimental site was constructed, consisting of ten cast-in-place piles with embedded defects. When analyzing field data, pile defects were not identified. For further analysis of the problem, as well as for interpreting the results and identifying pile defects, a cluster analysis method, the so-called ANN-classifier, is proposed. This paper describes the results of creating an algorithm for the recognition of defects and their localization in cast-in-place piles. It is proposed that use of the characteristic points of the spectrum of the signal as the input vector of the ANN classifier, and the type of pile defect as the output vector, is optimal. The results of the study led to the conclusion that the ANN-classifier can be used as the main tool for automatic interpretation of the results obtained by low strain integrity testing. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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20 pages, 3515 KiB

Open AccessArticle

OPC-UA Agent for Legacy Programmable Logic Controllers

by Seung-Yong Lee and Minyoung Sung

Appl. Sci. 2022, 12(17), 8859; https://doi.org/10.3390/app12178859 - 3 Sep 2022

Cited by 8 | Viewed by 2731

Abstract

Open platform communications (OPC) unified architecture (UA) is a communication standard increasingly used in industrial automation systems to enable the exchanging of control and management data between distributed entities. This paper proposes the design of an OPC-UA agent to enable UA information service [...] Read more.

Open platform communications (OPC) unified architecture (UA) is a communication standard increasingly used in industrial automation systems to enable the exchanging of control and management data between distributed entities. This paper proposes the design of an OPC-UA agent to enable UA information service and client functionalities in legacy programmable logic controllers (PLCs). The agent runs on a separate machine connected to the PLC using a dedicated link and maintains shared memory for certain variables in the PLC. Based on the periodically synchronized variables, the agent services the OPC-UA information model and executes client function blocks on behalf of the PLC. One important design feature is the remote procedure call of IEC 61131-3-based function blocks using synchronized variables. This allows the standard OPC-UA client functions to be used in existing PLCs which only support numeric types and do not support strings or complex structures. To validate the proposed design, we implement an agent prototype and demonstrate the successful monitoring and control of an industrial robot controller via OPC-UA. Through experiments, we evaluate the performance of UA functions in terms of the latency of read services for an increasing number of items. The evaluation results are believed to provide useful insights into agent-based approaches for integrating legacy PLCs into the OPC-UA framework. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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13 pages, 2122 KiB

Open AccessArticle

Design and Development of a I4.0 Engineering Education Laboratory with Virtual and Digital Technologies Based on ISO/IEC TR 23842-1 Standard Guidelines

by José-de-Jesús Cordero-Guridi, Luis Cuautle-Gutiérrez, Ricardo-Iván Alvarez-Tamayo and Santiago-Omar Caballero-Morales

Appl. Sci. 2022, 12(12), 5993; https://doi.org/10.3390/app12125993 - 13 Jun 2022

Cited by 18 | Viewed by 2538

Abstract

Knowledge transfer associated with education in the automotive manufacturing and production fields is challenging due to the requirements of physical prototyping of mechanical components and laboratory-assisted testing. In this regard, aspects of Industry 4.0 such as virtual environments and enhanced human–computer interaction have [...] Read more.

Knowledge transfer associated with education in the automotive manufacturing and production fields is challenging due to the requirements of physical prototyping of mechanical components and laboratory-assisted testing. In this regard, aspects of Industry 4.0 such as virtual environments and enhanced human–computer interaction have been studied as important resources to improve teaching practices and achieve the equivalent Education 4.0 paradigm. Within the context of modern manufacturing techniques in the Industry 4.0 era and advanced tools for analysis and mechanical design, the present work describes the development of a virtual/augmented reality (VR/AR) laboratory to support learning, training, and collaborative ventures related to additive manufacturing for the automotive industry. The development was performed in accordance with the guidelines of the ISO/IEC TR 23842-1 standard, to ensure that the academic programs and the conditions of use of the laboratory were optimal. Experiences with students through the development of industry-related automotive projects support confidence in the suitability of the laboratory and the expectation of positive outcomes for future developments. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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16 pages, 1871 KiB

Open AccessArticle

Peak Energy Reduction in Flow Shop including Switch-Off Policies and Battery Storage

by Paolo Renna and Sergio Materi

Appl. Sci. 2022, 12(5), 2448; https://doi.org/10.3390/app12052448 - 26 Feb 2022

Cited by 4 | Viewed by 1842

Abstract

The energy storage system is typically used in manufacturing systems with renewables to reduce the impact of the instabilities of these sources. The switch-off policies are proposed in the literature to reduce the energy used by the machines in the idle state. This [...] Read more.

The energy storage system is typically used in manufacturing systems with renewables to reduce the impact of the instabilities of these sources. The switch-off policies are proposed in the literature to reduce the energy used by the machines in the idle state. This study uses simulation models to investigate the potential application of the energy storage system with switch-off policies to reduce the energy costs related to the use over the peak power constraint. The manufacturing system analyzed is a flow line with the possibility of introducing two switch-off policies considering the two parameters of the battery (capacity and charge rate) and three levels of power fluctuations needed by the manufacturing operations. The results highlight that the charge rate and the power fluctuations of the manufacturing operations are the more important factors that impact the model studied. The switch-off policies have a positive impact and can reduce the energy use over the peak power constraint even at a slower charge rate, which means battery at a lower cost. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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20 pages, 6543 KiB

Open AccessArticle

Proposal of a Tool for Determining Sub- and Main Dimension Indicators in Assessing Internal Logistics Readiness for Industry 4.0 within a Company

by Peter Poor, Michal Zoubek and Michal Simon

Appl. Sci. 2021, 11(24), 11817; https://doi.org/10.3390/app112411817 - 13 Dec 2021

Cited by 1 | Viewed by 2648

Abstract

Key elements of Industry 4.0 are the digitization of products and production, enterprise information systems, robotic workplaces, communication infrastructure, and of course, employees. Industry 4.0 transforms production from stand-alone automated units to fully integrated automated and continuously optimized production environments. According to the [...] Read more.

Key elements of Industry 4.0 are the digitization of products and production, enterprise information systems, robotic workplaces, communication infrastructure, and of course, employees. Industry 4.0 transforms production from stand-alone automated units to fully integrated automated and continuously optimized production environments. According to the prediction of Industry 4.0, new global networks will be created based on the interconnection of production equipment into CPS systems. These systems will be the basic building block of the so-called “smart factories”, and will be able to exchange information autonomously, trigger the necessary actions in response to current conditions and mutually independent inspections. The aim of this article is to describe the issue of readiness models for the Industry 4.0 concept, which are commonly used as tools for conceptualizing and measuring the maturity of an organization or process related to a specific target state. Characteristic for the models is their use because, on this basis, it is possible to identify the current readiness for the concept of Industry 4.0 comprehensively in the whole company or in various sub-areas. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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21 pages, 2626 KiB

Open AccessArticle

Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0

by Brecht De Beelde, David Plets and Wout Joseph

Appl. Sci. 2021, 11(23), 11248; https://doi.org/10.3390/app112311248 - 26 Nov 2021

Cited by 6 | Viewed by 2324

Abstract

With the deployment of data-driven assembly and production factories, challenges arise in sensor data acquisition and gathering. Different wireless technologies are currently used for transferring data, each with different advantages and constraints. In this paper, we present a hybrid network architecture for providing [...] Read more.

With the deployment of data-driven assembly and production factories, challenges arise in sensor data acquisition and gathering. Different wireless technologies are currently used for transferring data, each with different advantages and constraints. In this paper, we present a hybrid network architecture for providing Quality of Service (QoS) in an industrial environment where guaranteed minimal data rates and maximal latency are of utmost importance for controlling devices and processes. The location of the access points (APs) is determined during the initial network-planning action, together with physical parameters such as frequency, transmit power, and modulation and coding schemes. Instead of performing network-planning just once before the network rollout, the network is monitored continuously by adding telemetry data to the frame header of all data streams, and the network is automatically reconfigured in real-time if the requirements are not met. By not using maximum transmit powers during the initial roll-out, more APs are needed, but coverage is guaranteed when new obstructions such as metallic racks or machinery are added. It is found that decreasing the transmit power by 6 dB gives the best trade-off between the number of required APs and network robustness. The proposed architecture is validated via simulations and via a proof-of-concept setup. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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19 pages, 3578 KiB

Open AccessArticle

A Simulator for Educating the Digital Technologies Skills in Industry. Part One. Dynamic Simulation of Technological Processes

by Natalia Koteleva, Valentin Kuznetsov and Natalia Vasilyeva

Appl. Sci. 2021, 11(22), 10885; https://doi.org/10.3390/app112210885 - 18 Nov 2021

Cited by 11 | Viewed by 2823

Abstract

Digital technology is being introduced into all areas of human activity. However, there are a number of challenges in implementing these technologies. These include the delayed return on investment, the lack of visibility for decision-makers and, most importantly, the lack of human capacity [...] Read more.

Digital technology is being introduced into all areas of human activity. However, there are a number of challenges in implementing these technologies. These include the delayed return on investment, the lack of visibility for decision-makers and, most importantly, the lack of human capacity to develop and implement digital technologies. Therefore, creating a digital training simulator for the industry is an actual task. This paper focuses on the first step in creating a digital training simulator for the industry: developing a dynamic process model. The process chosen is flotation, as it is one of the most common mineral processing methods. The simulation was performed in AVEVA Dynamic Simulation software. The model is based on a determination of reaction rate constants, for which, experiments were conducted on a laboratory pneumomechanical flotation machine with a bottom drive. The resulting model was scaled up to industrial size and its dynamic properties were investigated. In addition, the basic scheme of a computer simulator was considered, and the testing of the communication channels of a dynamic model with systems, equipment and software for digitalizing was conducted. The developed model showed acceptable results for its intended purpose, namely, an exact match to the technological process in terms of time. This helps to account for inertia and a fast response on all tested communication channels, as well as being acceptable for the real-time simulation speed of the solver. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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16 pages, 6363 KiB

Open AccessArticle

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

by Natalia Koteleva, Sergei Khokhlov and Ilia Frenkel

Appl. Sci. 2021, 11(22), 10848; https://doi.org/10.3390/app112210848 - 17 Nov 2021

Cited by 18 | Viewed by 2516

Abstract

Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach [...] Read more.

Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, trommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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16 pages, 4929 KiB

Open AccessArticle

Smart Design of Portable Indoor Shading Device for Visual Comfort—A Case Study of a College Library

by Jiao Xue, Yibo Wang and Mingxiang Wang

Appl. Sci. 2021, 11(22), 10644; https://doi.org/10.3390/app112210644 - 11 Nov 2021

Cited by 11 | Viewed by 3264

Abstract

With the development of architectural technology, the use of floor-to-ceiling windows has emerged widely. The ensuing problem is that more and more students and office workers are suffering from direct sunlight while working in specific areas. Based on the pain points of the [...] Read more.

With the development of architectural technology, the use of floor-to-ceiling windows has emerged widely. The ensuing problem is that more and more students and office workers are suffering from direct sunlight while working in specific areas. Based on the pain points of the working process, this study designed a portable product for improving visual comfort through field research and environment simulation. It provided a new personalized design for blocking direct sunlight from the working area using a portable and liftable sunshade curtain, allowing the users to control the height and angle of the sunshade curtain through a mobile phone application. It can also adjust itself according to environmental parameters collected by sensors, so as to block sunlight in certain areas. A simulation based on the design features and the light environment of a library is run, proving the model effective in improving these aspects. The study aimed to provide solutions for indoor visual comfort and suggestions for future indoor household designs. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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18 pages, 6028 KiB

Open AccessArticle

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

by Dongbeom Ko, Sungjoo Kang, Hyunsuk Kim, Wongok Lee, Yousuk Bae and Jeongmin Park

Appl. Sci. 2021, 11(21), 10376; https://doi.org/10.3390/app112110376 - 4 Nov 2021

Cited by 5 | Viewed by 3196

Abstract

This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, [...] Read more.

This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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16 pages, 5633 KiB

Open AccessArticle

A Study on Semantic-Based Autonomous Computing Technology for Highly Reliable Smart Factory in Industry 4.0

by Kwang-Jin Kwak and Jeong-Min Park

Appl. Sci. 2021, 11(21), 10121; https://doi.org/10.3390/app112110121 - 28 Oct 2021

Cited by 4 | Viewed by 1942

Abstract

Smart factories have made great progress with the development of various ICT technologies, such as IoT, big data, and artificial intelligence. The recent development of smart factory technology has shown results in automation and data acquisition and processing. However, it still has incomplete [...] Read more.

Smart factories have made great progress with the development of various ICT technologies, such as IoT, big data, and artificial intelligence. The recent development of smart factory technology has shown results in automation and data acquisition and processing. However, it still has incomplete points to be converted to advanced technology, including intelligence. For intelligentization, there is a need to propose a new research method in addition to the previous methodologies. Considering the specificity of the factory, the data structure and methodology of the Semantic Web can be effective. Therefore, in this study, a smart factory was designed by the convergence of monitoring technology, autonomous control technology, and semantic web technologies. Based on the proposed methodology, a methodology for the autonomous control of a smart factory on a digital twin was designed. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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19 pages, 8705 KiB

Open AccessArticle

The Effect of Training in Virtual Reality on the Precision of Hand Movements

by Sergo Martirosov, Petr Hořejší, Pavel Kopeček, Marek Bureš and Michal Šimon

Appl. Sci. 2021, 11(17), 8064; https://doi.org/10.3390/app11178064 - 31 Aug 2021

Cited by 8 | Viewed by 2485

Abstract

The main point of the work was to use virtual reality to discover its benefits on training, specifically on the precision of hand movements in specific settings, and then evaluate its effects both for virtual reality and the transfer of the results to [...] Read more.

The main point of the work was to use virtual reality to discover its benefits on training, specifically on the precision of hand movements in specific settings, and then evaluate its effects both for virtual reality and the transfer of the results to the real world. A virtual reality simulation was created using the Unity3D game engine and real-world experimental material was also prepared. A total of 16 participants took part in the training, which lasted for approximately one month. Once the data was gathered from both the virtual reality and real-world tests, we carried out in-depth statistical analysis. The results suggest positive outcomes in most aspects in virtual reality training productivity, but only partial transfer of the training benefits to the real world scenario. The possible reasons for this are described in the work and suggestions are given to duplicate the study with different variables to try to achieve different results. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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26 pages, 3340 KiB

Open AccessArticle

Evaluation of the Level and Readiness of Internal Logistics for Industry 4.0 in Industrial Companies

by Michal Zoubek and Michal Simon

Appl. Sci. 2021, 11(13), 6130; https://doi.org/10.3390/app11136130 - 30 Jun 2021

Cited by 16 | Viewed by 4079

Abstract

Industry 4.0 integrates modern technologies into independently functioning units and fundamentally changes established production and non-production processes. Industry 4.0 is also used in the field of logistics with the introduction of automation, robotics or modern warehouse systems with a key element of digitisation. [...] Read more.

Industry 4.0 integrates modern technologies into independently functioning units and fundamentally changes established production and non-production processes. Industry 4.0 is also used in the field of logistics with the introduction of automation, robotics or modern warehouse systems with a key element of digitisation. Development based on these principles presents huge challenges for the logistics sector as well as opportunities for further growth. Because the field of logistics is very large, it is important to be more specific. Internal logistics is a very important part of production processes; areas such as storage and supply of production lines with input materials are some of the key processes in a company. The implementation of Industry 4.0 principles is specific and, for many companies, demanding technologically, organisationally and financially. Therefore, companies must know their current level of logistics processes and evaluate the readiness of these elements for automation and digitisation. The company’s management should create a strategy which evaluates internal logistics processes for Industry 4.0. Company readiness will be evaluated on the basis of the tools (methodology) presented in the article. An objective assessment with a multi-level system is needed, and therefore internal logistics is structured into sub-areas. The implementation of this innovative method of evaluation in the industrial environment and a description of the tool development process will also be presented. As these are extensive issues, the initial sections cover the theoretical background of the topics that justify the need and novelty of this tool. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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24 pages, 886 KiB

Open AccessArticle

Industry 4.0 Maturity Model Assessing Environmental Attributes of Manufacturing Company

by Michal Zoubek, Peter Poor, Tomas Broum, Josef Basl and Michal Simon

Appl. Sci. 2021, 11(11), 5151; https://doi.org/10.3390/app11115151 - 1 Jun 2021

Cited by 30 | Viewed by 4387

Abstract

The primary purpose of this article is to present a maturity model dealing with environmental manufacturing processes in a company. According to some authors, Industry 4.0 is based on characteristics that have already been the focus of “lean and green” concepts. The goal [...] Read more.

The primary purpose of this article is to present a maturity model dealing with environmental manufacturing processes in a company. According to some authors, Industry 4.0 is based on characteristics that have already been the focus of “lean and green” concepts. The goal of the article was to move from resource consumption, pollutant emissions, and more extensive manufacturing towards environmentally responsible manufacturing (ERM). Using environmental materials and methods reduces energy consumption, which generates cost savings and higher profits. Here, value stream mapping (VSM) was applied to identify core processes with environmental potential. This paper provides an understanding of the role of environmental manufacturing in the era of the Fourth Industrial Revolution. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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14 pages, 2715 KiB

Open AccessArticle

Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

by Xixiang Wang and Jiafu Wan

Appl. Sci. 2021, 11(7), 3188; https://doi.org/10.3390/app11073188 - 2 Apr 2021

Cited by 17 | Viewed by 3156

Abstract

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The [...] Read more.

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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27 pages, 2010 KiB

Open AccessReview

Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

by Anbesh Jamwal, Rajeev Agrawal, Monica Sharma and Antonio Giallanza

Appl. Sci. 2021, 11(12), 5725; https://doi.org/10.3390/app11125725 - 20 Jun 2021

Cited by 226 | Viewed by 26417

Abstract

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety [...] Read more.

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies. Full article

(This article belongs to the Special Issue Advanced Design and Manufacturing in Industry 4.0)

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