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Engineering    2017, Vol. 3 Issue (5) : 616-630     https://doi.org/10.1016/J.ENG.2017.05.015
Research |
对工业4.0背景下的智能制造的回顾
钟润阳1,徐旬1(),Eberhard Klotz2,Stephen T. Newman3
1. Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand
2. Industry 4.0 Campaign, Festo AG & Co. KG, Esslingen 73726, Germany
3. Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
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摘要 作为新一代工业模式的工业4.0旨在提升生产灵活性,也将继续提高企业生产效率,保证更高的产品质量以及培养承担大规模定制的能力。因此,它能够使企业在短时间内生产出更高质量的产品,以应对日益个性化的产品带来的挑战。在工业4.0模式中,智能制造发挥着重要的作用。典型的资源被转换成智能实体,以便它们能够在智能环境中感知、行动和行为。为了能充分理解在在工业4.0背景下的智能制造,本文对智能制造、物联网(IoT)支持制造和云制造等相关课题进行了综合评述,在我们已有的分析基础上对其中的相似之处和不同点进行了重点探讨。在文中我们还回顾了一些用于实现智能制造的关键技术,如物联网、网络物理系统(CPS)、云计算、大数据分析(BDA)以及信息和通信技术(ICT)。之后我们将介绍全球智能制造业的发展动向,包括来自不同国家的政府战略计划以及来自欧盟、美国、日本和中国的主要跨国公司的战略计划。最后,我们提出其当前所面临的挑战和未来的研究方向。本文所讨论的概念将为实现备受期待的第四次工业革命带来新的思路。
 
关键词 智能制造工业4.0物联网制造系统物理信息系统    
Abstract

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

Keywords Intelligent manufacturing      Industry 4.0      Internet of Things      Manufacturing systems      Cloud manufacturing      Cyber-physical system     
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在线预览日期:    发布日期: 2017-11-08
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Ray Y. Zhong
Xun Xu
Eberhard Klotz
Stephen T. Newman
引用本文:   
Ray Y. Zhong,Xun Xu,Eberhard Klotz, et al. Intelligent Manufacturing in the Context of Industry 4.0: A Review[J]. Engineering, 2017, 3(5): 616-630.
网址:  
http://engineering.org.cn/EN/10.1016/J.ENG.2017.05.015     OR     http://engineering.org.cn/EN/Y2017/V3/I5/616
Fig.1  Statistics from Scopus database (search keywords: “intelligent manufacturing”; Date: 31 March 2017). (a) Published documents per year; (b) published documents by source; (c) published documents by affiliation; (d) published documents by author; (e) published documents by country/region.
Concepts Major characteristics Supporting technologies Major research Applications Refs.
Intelligent manufacturing ·AI-based smart decision-making
·Advanced automotive production
·Adaptive and flexible manufacturing systems
·Big data processing
·Advanced robotics
·Industrial connectivity services
·Last-generation sensors
·Advanced manufacturing decision-making models
·Human-machine integration
·AI-enabled machine learning
·Machine-to-machine connectivity
·A smart manufacturing system with a portrait of an ISO STEP tolerancing standard
·A product life-cycle test bed enabling intelligent manufacturing
·Agent-based IMSs
·Intelligent manufacturing planning and control systems
[11,39?42]
IoT-enabled manufacturing ·Auto-ID technology-based smart manufacturing system
·Real-time data collection
·Real-time visibility and traceability of production processes
·Real-time manufacturing decision-making
·IoT
·Wireless production
·BDA
·Cloud computing
·Real-time data-driven decision-making models
·Real-time data visualization
·SMO modeling
·Models of SMO behaviors
·An RFID-based resources management system
·An IoT-enabled smart construction production system
·An RFID-based job shop WIP inventories management system
·An RFID-enabled real-time production planning and scheduling system
[43?47]
Cloud manufacturing ·Manufacturing service distribution and sharing
·Intelligent capability management
·Manufacturing cloud service management
·Cloud computing
·IoT
·Virtualization method
·Service-oriented technology
·Modeling of manufacturing resources and capabilities
·Manufacturing services configuration
·Manufacturing cloud architecture
·Data visualization in a cloud manufacturing shop floor
·QoS-based service composition selection in a cloud manufacturing system
·Smart cloud manufacturing using the IoT
·A semantic web-based framework in cloud manufacturing
[33,48?50]
Tab.1  Comparisons of key concepts.
Industries/companies Aims Improvements Future research Refs.
Smart community, Canada and China ·Neighborhood watch
·Pervasive healthcare
·Value-added services such as utility management and social networking
·Suspicious event detection in neighborhood watch
·Cooperative authentication
·Detecting unreliable nodes
·Target tracking and intrusion detection
[58]
A cloud implementation using Aneka, Australia ·Sharing data between application developers
·IoT application-specific framework
·A seamless independent IoT working architecture
·Open and dynamic resource provisioning
·Integrated IoT and cloud computing
·Big data for IoT applications
[59]
Healthcare and social applications, USA ·Improving the quality of human life
·Examining potential societal impacts
·Enabling ambient intelligence
·Ubiquitous communication
·Increased processing capabilities
·IoT theory for management and operations
·IoT data complexity analysis
·IoT-enabled global business and commerce
[60]
Machine-to-machine measurement, Ireland and France ·Easing the interpretation of sensor data
·Combining domains
·Cross-domain connection
·Improved performance
·Enhanced interpretation from users
·Domain knowledge extraction
·Interoperable ontologies and datasets
[61]
Smart cities, Padova, Italy ·Providing open access to selected subsets
·Building an urban IoT system
·Improved energy efficiency
·Reduced traffic congestion
·Smart lighting and parking
·Smart city data analysis
·Smart connectivity
·System extension
[62]
IoT Gateway system, China ·Helping telecom operators transmit data
·Controlling functions for sensor network
·Improved functions such as data display, topology, etc.
·Enhanced data transmission
·Advanced IoT Gateway functions
·Security management
[63]
IoT application framework, India and France ·Developing an IoT application framework
·Implementing the methodology to support stakeholders’ actions
·Improved productivity of stakeholders
·Improved collaborative work
·Mapping algorithm cognizant of heterogeneity
·Developing concise notion for Srijan development language
·Testing support for IoT application development
[64]
IoT-enabled energy management, Italy and Spain ·Illustrating energy management at production level
·Proposing IoT-based energy management in production
·Providing a framework to support the integration of energy data
·Integrated energy data management
·Improved energy efficiency
·Enhanced energy data analysis
·Conventional hypothesis testing
·System extension
[65]
IoT-enabled real-time information capturing and integration framework, China ·Providing a new paradigm of IoT to manufacturing
·Designing a real-time manufacturing information integration service
·Real-time information capturing
·Improved logistics
·Optimal production using captured data
·Prediction model of production exceptions
[66]
Tab.2  Typical applications of IoT.
Industries/companies Aims Improvements Future research Refs.
Power systems, USA and Canada ·CPS test bed implemented in RTDS and OPNET ·Providing a realistic cyber-physical testing environment in real time ·Studying CPS vulnerabilities in various power system models [74]
Children keeper service, Korea ·Proposing a key design method for CPSs ·Designing CPSs with high-quality more feasibly and practically ·Data-driven CPS decision-making models [75]
Water distribution networks, USA ·Integrated simulation method for reflecting the operation and interaction of CP networks ·Facilitating modeling CPSs ·Extending the models and techniques for other CPS domains [76]
Civil structure, USA ·Developing and assessing CPSs for real-time hybrid structural testing ·Illustrating the feasibility of virtualizing CPS components ·Improving hydraulic actuator models
·Quantifying further scalability of the proposed approach
[77]
Fire handling. China ·Developing a simulation model for emergency handling problems ·Obtaining optimal sensing and robot scheduling policies ·Increasing computational time for more complicated scenarios [78]
Autonomous vehicles, USA and Germany ·Proposing a parallel programming model for CPSs ·Guaranteeing timeliness for complex real-time tasks ·Addressing the dynamic nature of CPSs in the proposed model [79]
Intelligent manufacturing, Sweden and USA ·Associating a CPS with holons, agents, and function blocks
·Using CPS to digitalize pneumatics with applications
·Ease of system implementation in decentralized or cloud environment
·Maximized flexibility and advanced condition monitoring
·Self-adjusting and self-adopting subsystem
·Practical in dynamic manufacturing with uncertainty
·Time-sensitive networking for synchronized motion control
·Distributed decision-making and self-organization between (sub)systems
[71,72,80]
Healthcare, Brazil ·Model-based architecture for validating medical CPSs ·Providing enough information to perform medical tests ·Proposing architecture for other medical device models [81]
Communication, China ·Analyzing the features of machine-to-machine, wireless sensor networks, CPS, and the IoT
·Reviewing home machine-to-machine networks
·Outlining the challenges related to CPS design ·Future design of CPSs [82]
Tab.3  Typical applications of CPS.
Industries/organizations Aims Improvements Future research Refs.
Business, France ·Proposing a method for cloud business applications ·Reducing the technical knowledge for provisioning cloud applications ·Integrating a discovery approach and semantic matching in the components discovery phase
·Adding a negotiator module
[103]
National Natural Science Foundation, China ·Presenting a hybrid information fusion approach ·Achieving multilayer information fusion
·Identifying global sensitivities of input factors under uncertainty
·More comprehensive information fusion approach [104]
Business and healthcare, UK ·Developing cloud computing in the life sciences ·Introducing cloud models to life-science business ·Identifying major issues [105]
IT and business, UK ·Highlighting aspects and uniqueness of cloud computing ·Examining the true benefits and costs of cloud computing ·Application extension in other industries [106]
Manufacturing, Iran ·Proposing a service-oriented approach ·Adopting a layered platform (LAMMOD) for distributed manufacturing agents ·Upgrading the XMLAYMOD layers’ procedures and structures [107]
Education, India ·Outlining the benefits of using cloud computing for students ·Providing opportunities for students to test, learn, experiment, and innovate ·More cloud-based education applications [108]
ICT, China ·Proposing a forensic method for efficient file extraction ·Efficient location of large files stored across data nodes ·Researching the parallel extraction method for a Hadoop distributed file system
·Researching the analysis method on EditLogs
[109]
ISO-New England, USA ·Developing cloud-based power system simulation platform ·Security schemes
·Cost savings
·Real-life applications of this system [110]
Transportation, China ·Formulating a new entropy-cloud approach ·Solving the railway container station reselection problem ·Study, design, and plan for the transferring network [111]
Tab.4  Typical applications of cloud computing.
Industries/companies Aims Improvements Future research Refs.
Google, USA ·Refining its core search and ad-serving algorithms ·Searching patterns and recommended searches based on what others have searched, external events, and etc. ·Studying the algorithm [121]
Retailers, UK and USA ·Tesco: precise promotions and strategic segmentation of customers
·Amazon: accurate recommendations for customers
·Wal-Mart: supply-chain optimization
·Mining customer data from loyalty program
·Recommendation engine based on collaborative filtering
·Enabling vendor-managed inventory based on big data
·Reducing potential risks of sharing data
·Avoiding using sensitive personal information
·Protecting IT infrastructure from cyber attacks
[112]
Biopharmaceutical industry, USA ·Reducing process flaws
·Eliminating yield variation
·Making targeted process changes according to statistical analysis
·Increasing its vaccine yield by more than 50%
·Making a long-term investment in systems to collect more data
·More advanced analytics
[120]
Remote monitoring application for heavy-duty equipment vehicle, USA ·Assessing and predicting the health of the diesel engine component ·Utilizing classification model to detect analogous engine behavior
·Fuzzy logic-based algorithm for remaining life prediction
·Predictive manufacturing process
·More comprehensive big data environment
[122]
Tata Motor, India ·Driving quality and reducing cost in manufacturing process
·Increasing customer satisfaction level
·Utilizes process excellence and Six Sigma principles
·Analytics of CRM system data
·Combination of optimization, emotion, and empathic use of data [118]
Premier Healthcare Alliance (vendor: IBM), USA ·Improving patient outcomes
·Reducing expenditure
·Collecting data from different departmental systems and sending to central data warehouse
·Generating reports to help users recognize emerging healthcare issues by data processing
·Developing efficient unstructured data analytical algorithms and applications [123]
General Electric (Global Software and Analytics Center), USA ·Boosting industrial product sales
·Reducing after-sale maintenance cost
·Optimizing the service contracts and maintenance intervals for industrial products ·Integration with data processing in production process [121]
Aerospace industry, USA ·Predicting number of returns in the future
·Minimizing product escapes
·Combining large datasets (manufacturing and repair) together
·Using predictive algorithm to analyze data in aerospace test environments
·Automated process of datasets combination [124]
Tab.5  Typical applications of BDA.
Industries/companies Aims Improvements Future research Refs.
Nigerian national policy analysis, Nigeria ·Examining the ICT impacts on education
·Determining suitable policy for ICT potential in the Nigerian education system
·Integration in teaching and learning
·Improving teachers’ professional development
·Maximizing ICT potential
·Proper ICT implementation and monitoring
[129]
Foresight processes, Delphi, Germany ·Identifying the channels for ICT in foresight
·Determining the focus on foresight processes using ICT
·More precise strategic decision-making
·Increasing product variety in ICT-based foresight tools
·Insights concerning specific tools
·Expanding the scope
[130]
Job satisfaction evaluation, USA ·Examining the association between ICT factors and job satisfaction
·Examining technology orientation impacts
·Improving sales and job satisfaction
·Integrating ICT tools in daily professional activities
·ICT-enabled training
·Educational influence of ICT
[131]
Tourism, Hong Kong, China ·Establishing the process of ICT in tourism ·Improving hospitality in tourism
·Improving tourism services
·Industry applications
·Incorporating ICT into business missions
[132]
Water and soil monitoring, Taiwan, China ·Using ICT to efficiently improve monitoring systems
·Classifying the focal area into different agricultural environmental risk zones
·Improving environmental assessments and environmental management decisions
·Increasing awareness of ecosystem services
·Collecting data analytics
·Increasing the potential of environmental monitoring coverage
[133]
Nursing education, Australia ·Examining e-learning with ICT
·Finding the impact of ICT changes on nursing education
·Improving learning efficiency
·Increasing motivation for learning
·Learning-quality evaluation
·Preregistration nursing curricula
[134]
Women’s primary healthcare, Brazil ·Analyzing the ICT incorporation in primary care
·Identifying different aspects associated with better quality in the care
·Improving women’s healthcare
·Improving ICT resources utilization
·Incorporation and the quality of primary healthcare
·Policies implementation
[135]
Emergency medical services, China ·Storing and interpreting data
·Building an ICT system for emergency medical services
·Improving emergency medical rescuing processes
·Increasing data access
·Applying standard data models
·Short value chain
[136]
ICT-enabled manufacturing landscape, Germany ·Examining industry decision-making using ICT ·Improving decision-making efficiency
·Improving product quality
·Decreasing time-to-market
·Allocating production capacity within a value chain
·Establishing of a heterogeneous tool environment
[137]
Tab.6  Typical applications of ICT.
Fig.2  A framework of the Industry 4.0 IMS.
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