Special Issue "Novel Industry 4.0 Technologies and Applications"

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

Deadline for manuscript submissions: closed (22 June 2020).

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

Special Issue Editors

Dr. Nikolaos Papakostas
E-Mail Website
Guest Editor
Laboratory for Advanced Manufacturing Simulation and Robotics (LAMS), School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: digital manufacturing; manufacturing simulation; robotics; assembly processes; production planning and control
Special Issues and Collections in MDPI journals
Prof. Carmen Constantinescu
E-Mail Website
Guest Editor
Fraunhofer Institute for Industrial Engineering – FhG IAO, Stuttgart, Germany
Interests: digital/virtual/smart factory; Manufacturing 4.0; modeling; simulation
Prof. Dr. Dimitris Mourtzis
E-Mail Website
Guest Editor
Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rion-Patras, Greece
Interests: manufacturing systems planning, control, and networking; design of manufacturing systems and networks; flexibility and complexity in manufacturing systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 paradigm has been characterized by greater connectivity between networks of digitalized manufacturing systems. The application of enabling technologies, including automation and cyber-physical systems, has supported smart manufacturing and decentralized decision making. The implications of Industry 4.0 technologies are significant, leading to reduced production time and cost, while improving product quality. The challenges include how to analyze, exchange, and securely manage the vast amounts of data generated between manufacturing systems.

These challenges have spurred growth in research areas including additive manufacturing, Artificial Intelligence, collaborative robotics, digital manufacturing, Internet of Things, machine learning, Big Data analytics, virtual and augmented reality, as well as many others. This Special Issue will focus on novel Industry 4.0 technologies, which will enable smart manufacturing systems and “Factories of the Future”.

Keywords

We welcome the submission of papers in the following potential topics, as well as other related topics:

  • Additive and hybrid manufacturing;
  • Artificial Intelligence;
  • Augmented/virtual reality in manufacturing;
  • Big Data analytics for manufacturing applications;
  • Blockchain and information security systems in manufacturing;
  • Cloud computing and manufacturing;
  • Computer vision for automation;
  • Cyber-physical systems;
  • Digital manufacturing and product lifecycle management systems;
  • Digital twin simulation models;
  • Internet of Things (IoT);
  • Machine learning for manufacturing and supply chain management applications;
  • Manufacturing process automation and simulation;
  • Multiagent systems in manufacturing and supply chains;
  • Robotics and autonomous systems.

Prof. Nikolaos Papakostas
Prof. Carmen Constantinescu
Prof. Dimitris Mourtzis
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 papers will be 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 2000 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.

Published Papers (14 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Editorial
Novel Industry 4.0 Technologies and Applications
Appl. Sci. 2020, 10(18), 6498; https://doi.org/10.3390/app10186498 - 17 Sep 2020
Viewed by 784
Abstract
The Industry 4 [...] Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)

Research

Jump to: Editorial, Review

Article
Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation
Appl. Sci. 2020, 10(15), 5150; https://doi.org/10.3390/app10155150 - 27 Jul 2020
Cited by 12 | Viewed by 1092
Abstract
Due to Industry 4.0 technologies, small- and medium-sized enterprises have a great opportunity to increase their competitiveness. However, the question remains as to whether they are truly able to implement such modern technologies faster and carry out digital transformation. The main aim of [...] Read more.
Due to Industry 4.0 technologies, small- and medium-sized enterprises have a great opportunity to increase their competitiveness. However, the question remains as to whether they are truly able to implement such modern technologies faster and carry out digital transformation. The main aim of the paper is to classify small- and medium-sized enterprises into various groups, according to the level of implementation of Industry 4.0, using the Index of Industry 4.0. Based on the results of the cluster analysis, the small and medium enterprises are categorized into four different groups, according to the level of implementation of Industry 4.0. There are top Industry 4.0 technological enterprises, I4 start enterprises, noobs enterprises, and I4 advances enterprises. So far, the largest group consists of the small- and medium-sized enterprises that are just starting out with the introduction of Industry 4.0 technologies, such as IT infrastructure, digitalization (data, cloud, data analysis, and information systems), and sensors. On the other hand, the top I4 technological enterprises group is the least numerous. The analysis carried out comparing the small- and medium-sized enterprises with the large enterprises shows that the SMEs still have a lower level of Industry 4.0 implementation. This confirms the assumption that the large enterprises have greater opportunities to use new technologies and transform them into smart factories. However, this situation may change in the future if new technologies become more accessible, and SMEs are worth investing in Industry 4.0 in terms of the return on investment. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
An Agent-Based Decision Support Platform for Additive Manufacturing Applications
Appl. Sci. 2020, 10(14), 4953; https://doi.org/10.3390/app10144953 - 18 Jul 2020
Cited by 1 | Viewed by 786
Abstract
The effective estimation and consideration of process cost, time, and quality for additive manufacturing operations, when a series of suitable technologies and resources are available, is very important for making informed product design and development decisions. The main objective of this paper is [...] Read more.
The effective estimation and consideration of process cost, time, and quality for additive manufacturing operations, when a series of suitable technologies and resources are available, is very important for making informed product design and development decisions. The main objective of this paper is to propose the design, deployment, and use of an agent-based decision support platform, which is capable of proposing alternative additive manufacturing resources and process configurations to design engineers while reducing the number of communication steps among engineering teams and organizations. Different computer-aided systems are utilised and interfaced for automating the information exchange as well as for accelerating the overall product development process. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)
Appl. Sci. 2020, 10(13), 4442; https://doi.org/10.3390/app10134442 - 27 Jun 2020
Cited by 4 | Viewed by 867
Abstract
Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and [...] Read more.
Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby’s law of requisite variety and Vigotsky’s activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT
Appl. Sci. 2020, 10(7), 2377; https://doi.org/10.3390/app10072377 - 31 Mar 2020
Cited by 5 | Viewed by 1279
Abstract
A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to [...] Read more.
A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Real-Time Remote Maintenance Support Based on Augmented Reality (AR)
Appl. Sci. 2020, 10(5), 1855; https://doi.org/10.3390/app10051855 - 08 Mar 2020
Cited by 12 | Viewed by 1529
Abstract
In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended [...] Read more.
In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended Reality (XR) have become mainstream including Augmented Reality (AR). Furthermore, Cloud Computing has enabled the provision of high-quality services, especially in the controversial field of maintenance. However, since modern machines are becoming more complex, maintenance must be carried out from experienced and well-trained personnel, while overseas support is timely and financially costly. Although AR is a back-bone technology facilitating the development of robust maintenance support tools, they are limited to the provision of predefined scenarios, covering only a limited number of scenarios. This research work aims to address this emerging challenge with the design and development of a framework, for the support of remote maintenance and repair operation based on AR, by creating suitable communication channels between the shop-floor technicians and the expert engineers who are utilizing real-time feedback from the operator’s field of view. The applicability of the developed framework is tested in vitro in a lab-based machine shop and in a real-life industrial scenario. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Methodology of Employing Exoskeleton Technology in Manufacturing by Considering Time-Related and Ergonomics Influences
Appl. Sci. 2020, 10(5), 1591; https://doi.org/10.3390/app10051591 - 27 Feb 2020
Cited by 4 | Viewed by 1010
Abstract
This article presents a holistic methodology for planning, optimization and integration of exoskeletons for human-centered workplaces, with a focus on the automotive industry. Parts of current and future challenges in this industry (i.e., need of flexible manufacturing but as well having demographic change) [...] Read more.
This article presents a holistic methodology for planning, optimization and integration of exoskeletons for human-centered workplaces, with a focus on the automotive industry. Parts of current and future challenges in this industry (i.e., need of flexible manufacturing but as well having demographic change) are the motivation for this article. This challenges should be transformed in positive effectiveness by integrating of exoskeletons regarding this article. Already published research work from authors are combined in a form of summary, to get all relevant knowledge, and especially results, in a coherent and final context. This article gives interested newcomers, as well as experienced users, planners and researchers, in exoskeleton technology an overview and guideline of all relevant parts: from absolute basics beginning until operative usage. After fixing the motivation with resulting three relevant research questions, an introduction to the exoskeleton technology and to the current challenges in planning and optimizing the ergonomics and efficiency in manufacturing are given. A first preselection method (called ExoMatch) is presented to find the most suitable exoskeleton for workplacesm by filtering and matching all the important analyzed attributes and characteristics under consideration to all relevant aspects from environments. The next section treats results regarding an analysis of influencing factors by integrating exoskeletons in manufacturing. In particular, ergonomic-related and production-process-related (especially time-management) influences identified and researched in already published works are discussed. The next important step is to present a roadmap as a guideline for integration exoskeleton. This article gives relevant knowledge, methodologies and guidelines for optimized integrating exoskeleton for human-centered workplaces, under consideration of ergonomics- and process-related influences, in a coherent context, as a result and summary from several already published research work. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm
Appl. Sci. 2020, 10(4), 1381; https://doi.org/10.3390/app10041381 - 19 Feb 2020
Cited by 5 | Viewed by 837
Abstract
Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low [...] Read more.
Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Cutting Path Planning Technology of Shearer Based on Virtual Reality
Appl. Sci. 2020, 10(3), 771; https://doi.org/10.3390/app10030771 - 22 Jan 2020
Cited by 6 | Viewed by 638
Abstract
With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of [...] Read more.
With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of the shearer on the three-dimensional (3D) roof and floor based on the virtual reality engine (Unity3D). Firstly, the virtual 3D coal seam was constructed through the 3D geological coordinate data of the mine. On this basis, the shape function of the scraper conveyor with the adaptive configuration on the floor was constructed to obtain the combined operation of the virtual shearer and the scraper conveyor. The movement of the shearer’s walking and height-adjustment was then, analyzed. A strategy for automatic height-adjustment based on the adjustment of the direction of the drum movement is hence, proposed to control the cutting path of the shearer. Finally, different experimental schemes were simulated in the developed prototype system after which each of the schemes was evaluated using the fuzzy comprehensive evaluation method. The results show that the proposed strategy for trajectory control can improve the accuracy and stability of the shearer’s motion trajectory. In Unity3D, the pre-selected schemes and digital and visual planning of coal production are previewed ahead of time, the whole production process can be mapped synchronously in the production process. It is also obtained that the virtual preview and evaluation of the production process can provide some guidance for actual production. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Statistical Process Control with Intelligence Based on the Deep Learning Model
Appl. Sci. 2020, 10(1), 308; https://doi.org/10.3390/app10010308 - 31 Dec 2019
Cited by 8 | Viewed by 1439
Abstract
Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The [...] Read more.
Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
Appl. Sci. 2019, 9(24), 5405; https://doi.org/10.3390/app9245405 - 10 Dec 2019
Cited by 18 | Viewed by 1330
Abstract
Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of [...] Read more.
Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of the paper is to characterize the readiness of the enterprise to use Industry 4.0. In the research, a questionnaire survey was carried out on a sample of 276 enterprises mainly from the manufacturing industry. Using explorative factor analysis, the index of Industry 4.0 (VPi4) was designed to determine the level of Industry 4.0 implementation in the enterprises. The results were further verified by a statistical analysis, using Mann–Whitney test and correlation coefficients. The results indicate that the VPi4 index was consistent in terms of distribution when comparing the results on the verification sample. Its results correlate with the subjective perception of the enterprises, and different levels of the index reflect the difference in technological intensity of the industry. The VPi4 index enables the enterprises to determine their own level of current state of readiness for Industry 4.0, to better prioritize business development. The proposed solution categorizes Industry 4.0 components into a useful theoretical framework. Further research offers the possibility of applying the index in other sectors, its relation to the size of enterprises, and updating with respect to new trends in information technology. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Cloud-Based Collaborative 3D Modeling to Train Engineers for the Industry 4.0
Appl. Sci. 2019, 9(21), 4559; https://doi.org/10.3390/app9214559 - 27 Oct 2019
Cited by 8 | Viewed by 1078
Abstract
In the present study, Autodesk Fusion 360 software (which includes the A360 environment) is used to train engineering students for the demands of the industry 4.0. Fusion 360 is a tool that unifies product lifecycle management (PLM) applications and 3D-modeling software (PDLM—product design [...] Read more.
In the present study, Autodesk Fusion 360 software (which includes the A360 environment) is used to train engineering students for the demands of the industry 4.0. Fusion 360 is a tool that unifies product lifecycle management (PLM) applications and 3D-modeling software (PDLM—product design and life management). The main objective of the research is to deepen the students’ perception of the use of a PDLM application and its dependence on three categorical variables: PLM previous knowledge, individual practices and collaborative engineering perception. Therefore, a collaborative graphic simulation of an engineering project is proposed in the engineering graphics subject at the University of La Laguna with 65 engineering undergraduate students. A scale to measure the perception of the use of PDLM is designed, applied and validated. Subsequently, descriptive analyses, contingency graphical analyses and non-parametric analysis of variance are performed. The results indicate a high overall reception of this type of experience and that it helps them understand how professionals work in collaborative environments. It is concluded that it is possible to respond to the demand of the industry needs in future engineers through training programs of collaborative 3D modeling environments. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Article
Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation
Appl. Sci. 2019, 9(16), 3216; https://doi.org/10.3390/app9163216 - 07 Aug 2019
Cited by 7 | Viewed by 954
Abstract
To increase the reliability and accuracy of tolerance design, more and more research works are considering not only orientation and position deviations; they are also forming errors in tolerance modeling. As a direct cause of form errors in industrial mass production, the processing [...] Read more.
To increase the reliability and accuracy of tolerance design, more and more research works are considering not only orientation and position deviations; they are also forming errors in tolerance modeling. As a direct cause of form errors in industrial mass production, the processing features of the machining system degrade over time. Under the Industry 4.0 paradigm, an assembly tolerance design method based on Skin Model Shape is proposed to take the effect of degrading processing features into consideration. A continuous-time multi-dimensional Markov process is trained through maximum likelihood estimation based on the nodal sampling point set on the machined surface. Degradation of the machined surface is modeled based on the joint probability distribution of nodal displacements. Assembly force constraints and assembly entity constraints are applied to spatial assembly simulations. Tolerance synthesis takes the manufacturing cost and assembling probability as design objectives. A design example of the rotary feed component in a five-axis machine tool is proposed for explanation and verification. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Review

Jump to: Editorial, Research

Review
Review and Development Trend of Digital Hydraulic Technology
Appl. Sci. 2020, 10(2), 579; https://doi.org/10.3390/app10020579 - 13 Jan 2020
Cited by 3 | Viewed by 815
Abstract
Since the emergence of digital hydraulic technology, it has achieved good results in intelligence, integration, energy saving, etc. After decades of development, and it has also attracted wide attention in the industry. However, for many years, the definition of digital hydraulic technology has [...] Read more.
Since the emergence of digital hydraulic technology, it has achieved good results in intelligence, integration, energy saving, etc. After decades of development, and it has also attracted wide attention in the industry. However, for many years, the definition of digital hydraulic technology has differed between researchers, and there is no uniform definition. Such a situation affects the development of it to a certain extent. Therefore, this paper gives the exact definition of digital hydraulic technology based on a large number of researches on it. At the same time, the paper analyzes the research status and developmental process of the such a technology, and we forecast the development trend of it. Full article
(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)
Show Figures

Figure 1

Back to TopTop