Enterprise Information Systems Cloud

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Cloud manufacturing: a new manufacturing paradigm Lin Zhang Ren Hu

a b

a b

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, Yongliang Luo

, Xuesong Zhang

& Yongkui Liu

a b

a b

, Fei Tao

, Hua Guo

a b

a b

, Bo Hu Li

, Ying Cheng

a b c

a b

, Lei

, Anrui

a b

a

School of Automation Science and Electrical Engineering, Beihang University , Beijing , 100191 , P.R. , China b

Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beihang University , Beijing , 100191 , P.R. , China c

Beijing Simulation Center , Beijing 100854 , P.R. , China Published online: 21 May 2012.

To cite this article: Lin Zhang , Yongliang Luo , Fei Tao , Bo Hu Li , Lei Ren , Xuesong Zhang , Hua Guo , Ying Cheng , Anrui Hu & Yongkui Liu (2014) Cloud manufacturing: a new manufacturing paradigm, Enterprise Information Systems, 8:2, 167-187, DOI: 10.1080/17517575.2012.683812 To link to this article: http://dx.doi.org/10.1080/17517575.2012.683812

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Enterprise Information Systems, 2014 Vol. 8, No. 2, 167–1 87, http://dx.doi.org/10.1080/17517575.2012.683812

Cloud manufacturing: a new manufacturing paradigm Lin Zhanga,b*, Yongliang Luoa,b, Fei Taoa,b, Bo Hu Lia,b,c, Lei Rena,b, Xuesong Zhanga,b, Hua Guoa,b, Ying Chenga,b, Anrui Hua,b and Yongkui Liua,b a

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School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P.R. China; bEngineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beihang University, Beijing 100191, P.R. China; cBeijing Simulation Center, Beijing 100854, P.R. China (Received 19 December 2011; final version received 5 April 2012) Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing (CMfg) – for solving the bottlenecks in the informatisation development and manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied, and the constructing method for manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors’ group on CMfg are briefly presented. Keywords: cloud manufacturing (CMfg); concept; manufacturing cloud service; manufacturing cloud; cloud manufacturing service platform

1. Introduction Modern manufacturing has changed significantly due to intense global competition, economy globalisation, resource globalisation and rapid development of advanced manufacturing, information, computer and management technologies. The key mission of manufacturing has changed from enlarging production scale in 1960s, reducing production cost in 1970s, promoting product quality in 1980s, responding to market rapidly in 1990s, to emphasising knowledge and services in the present decade (Tao et al. 2011a,d). The introduction of computer and information technologies in manufacturing and the rapid development and application of Internet technologies have speeded up the development of manufacturing. To fulfil the target of TQCSEK (i.e. fastest Time-to-market, highest Quality, lowest Cost, best Service, cleanest Environment and high Knowledge), many manufacturing models and technologies have been proposed and researched. Computer Integrated Manufacturing (CIM) (Laplante 2005, Xu et al. 2005, Kalpakjian and Schmid 2006, Zhang et al. 2011) is the manufacturing approach of using computers to control the entire production process. This integration allows individual processes to exchange

*Corresponding author. Email: [email protected]

© 2013 Taylor & Francis

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information with each other and initiate actions. Through the integration of computers, manufacturing can be faster and less error-prone, although the main advantage is the ability to create automated manufacturing processes. Agile Manufacturing (AM) (Preiss 1994, Yusuf et al. 1999) is a term applied to an organisation that has created the processes, tools and training to enable it to respond quickly to needs of the customer and changes in the market while still controlling costs and quality. An enabling factor in becoming an agile manufacturer has been the development of manufacturing support technology that allows the marketers, the designers and the production personnel to share a common database of parts and products and to share data on production capacities and problems – particularly where small initial problems may have larger downstream effects. Concurrent Engineering (CE) (Abdalla 1999, Xu et al. 2007, Valle and Va´zquez-Bustelo 2009) is to consider factors associated with the life cycle of a product in the design phase. These factors include product functionality, manufacturing, assembly, testing, maintenance, reliability, cost and quality. The essence of CE is not only the concurrency of the activities but also the cooperative effort from all the involved teams, which leads to improving profitability and competitiveness. Networked Manufacturing (NM) (Montreuil et al. 2000) is proposed based on Internet and aims to make full sharing and cooperative of manufacturing resources distributed in different places. Application Service Provider (ASP) (Flammia 2001, Smith and Rupp 2002) is a business model that provides computer-based services to customers over a network. Virtual Manufacturing (VM) (Kazuaki et al. 1997, Park and Favrel 1999, Xu et al. 2002, Tan et al. 2010) is the nature realisation of actual manufacture process in the computer. In essence, VM will ultimately provide a modelling and simulation environment so powerful that the fabrication/assembly of any product, including the associated manufacturing processes, can be simulated in the computer. Industrial Product-Service System (IPS2) (Meier et al. 2010) mainly aims at application in business field and is a kind of socialisation technology system satisfying knowledge integration, which can realise high integration of mutual reliant and interactional products and services. Manufacturing Grid (MGrid) (Tao et al. 2008a,b, 2009a-c, 2010a-c, 2011c) is to overcome the barrier resulting from spatial distances in collaboration among different corporations to allow dispersed manufacturing resources (including design, manufacturing, human and application system resources) to be fully connected and shared. Crowd Sourcing (CS) is the act of outsourcing tasks, traditionally performed by an employee or contractor, to an undefined, large group of people or community (a ‘crowd’), through an open call. Besides, there are still many other technologies and models proposed for manufacturing in the past decade (Li and Li 2000, Li et al. 2000, Ulieru et al. 2000, Feng et al. 2001, 2003, Dan et al. 2005, Li 2007, Newman et al. 2008, Tan et al. 2008, Wang and Xu 2008, Xu et al. 2008, Chen et al. 2011, Wang et al. 2007, 2011, Xu and Xu 2011, Yin and Xie 2011). Each aforementioned advanced manufacturing technology or model has its own emphasis and has played an important role in the development of manufacturing industries. However, there are still many bottlenecks for them to be widely applied to manufacturing industries. 1.1. Issue of service model The issue of services model primarily involves (a) lacking of centralised management and operation of services, (b) lacking of distribution mechanism of profit, (c) the efficiency, quality and timeliness of service are hardly to be guaranteed. The research

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of present NM mode (e.g. ASP and MGrid) primarily focuses on how to connect those distributed manufacturing resources by network, which emphasizes how to integrate resource so that a manufacturing task can be finished coordinately. And a great deal of heterogeneous manufacturing resources at different physical locations is connected by sharing network to form a virtual resources pool, and then the manufacturing resource sharing is provided for manufacturing enterprises. In practical applications, a complex manufacturing task is decomposed into several simple tasks. These simple tasks are parallelly operated in different manufacturing resources nodes by scheduling mechanism. The execution results are integrated finally, which embodies the idea of ‘distributed resources are centralised to be used.’ However, the benefit of resources service provider (RSP) cannot be guaranteed because of the lack of centralised management and operation of services. Many RSPs are reluctant to share these enterprise manufacturing resources and have no incentive to provide high-quality manufacturing services, which make it difficult to guarantee the efficiency, quality and reliability of NM services. It is difficult to guarantee the manufacturing quality and timeliness of resources service demander (RSD) without continuous, stable and high-quality manufacturing services. As a result, RSP and RSD are unsatisfied with the existing network manufacturing service models, which hinder the application and promotion of NM. 1.2. Issue of resource sharing and optimal allocation The research of sharing and optimal allocation technologies of manufacturing resources includes resource optimal allocation problem, intelligent and dynamic match problem, intelligent embedded connection and accessing of physical manufacturing resources. At present, NM reflects a whole independent system on resource sharing and distribution. It provides services to users through resources of fixed number or solutions, whose business process is relatively fixed. With the development of network technology and the proposition of service-oriented architecture (SOA) (Wang et al. 2010), sharing and application of resource should be the forming process of business chain based on services, and then the organisation of a virtual enterprise (VE) is mapped. The solutions should be generated dynamically, i.e. the VE is formed after business process. In addition, the existing researches on NM have carried out a large amount of works about how to publish and search resources. But it lacks effective methods to realise intelligently match, rent-seek and dynamically composition of resource and tasks from the aspect of interface, function, flow, semantics and QoS (quality of service) agreement. In addition, the published and encapsulated manufacturing resources are primarily ‘soft resources’ (e.g. computing and information resource including software application system and manufacturing data) at present. But there is no satisfactory solution to realise the intelligent embedded accessing, encapsulation and invoking of terminal physical device. 1.3. Issue of free circulation and cooperation of manufacturing resources and capabilities The existing NM has achieved resource sharing in a certain extent. However, the shared resources are limited, which is only limited to the internal of enterprise or expanded E2E. The current challenges of promoting and applying manufacturing network model are how to share more manufacturing resources and capabilies in a

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wider range, realise barrier-free transaction and circulation of manufacturing resource and capability and enable them to be used on-demand. Only when the free transaction and circulation and on-demand use of manufacturing resource and capability are achieved, the transformation from production-oriented manufacturing to service-oriented manufacturing (SOM) can be finally realised. In addition, the safety and security problems by using NM needs to be solved, including system and platform security, network communication security, enterprise core data security, trust safety among users, credible manufacturing, preventing malicious attacks and destruction of third party. In the promotion and application process of NM, systems and platforms have potential safety hazard, potential safety hazard of network communication and trust problem between RSP and RSD. As a result, these problems seriously hinder the application of NM. The profits brought to enterprise by NM are not large at present. The specialised safety products developed by large IT enterprise for NM are extremely deficient. In brief, current manufacturing models lack (a) open and flexible architecture, common standards and specifications, (b) effective operational mechanisms supporting dynamic resource and capability circulation and cooperation and (c) the reliable safety solutions. As a result, the number, capability and utilisation modes of services provided to users are limited, and potential applications of these manufacturing models are hindered. Fortunately, a new service computing model, i.e. cloud computing (Chen and Zheng 2009), has emerged and has been widely applied in various fields. The idea of cloud computing is to construct the computer storage and computing service centre by specified computer and network company (the third-party service operator), and virtualised resources are stored as ‘cloud’ to provide services for users. In technology, cloud computing is an extension of virtualisation and Grid computing, but the transformation of service mode brought by the idea of cloud computing is more important, which enables computing resources to become a kind of special services and be provided in the way of information. With the proposition of cloud computing mode and the increasing maturity of its applications, enterprises also saw the huge profits. Therefore, many established enterprises, such as Google, IBM, Amazon, Yahoo, etc., have invested a lot of capital and manpower to carry out their own cloud computing plans and projects. At present, a number of application modes have emerged, typical of which are software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), utility computing, network service, manage service provider (MSP), and business service platform, Internet integration and cloud simulation platform (Li et al. 2009). Following the concept of cloud computing, the idea of cloud security was proposed. Some large information safety companies, such as Rising and Trend Micro, have invested heavily in special cloud security products. At present, some matured safety products are available. For example, in order to realise cloud security architecture, the international information security manufacturer Trend Micro has invested 400 million dollar to conduct five large-scaled cloudend data centres and install 34,000 cloud servers all over the world [http:// datacenter.chinabyte.com/168/9003168.shtml]. The cloud-end security antivirus software provided by Rising has been used by users, and it is said that its users have been over 80 million [http://security.zdnet.com.cn/security_zone/2009/0728/ 1419875.shtml]. From the above analysis, cloud computing provides new ideas and opportunities for solving the current problems existing in NM. The cloud security mode and the

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commercialised cloud security products provide new technical approaches for solving the security problems of NM. Furthermore, the development of embedded system and techniques provides enabling technology for implementing the intelligent embedded access of the terminal physical devices. In addition, the rapid development of the Internet of things (IoT) (Ning et al. 2006) can not only achieve the interconnection of traditional information but also promote the interconnection of physical information with the support of embedded technology and RFID technology, which provides the possibility for constructing a comprehensive interconnected manufacturing resource sharing platform (Kumar et al. 2011, Xu 2011a,b). Moreover, the development and applications of high performance computing (HPC) technology also provide the possibility for solving more complex manufacturing problems and carrying out large-scale collaborative manufacturing. Therefore, drawing by the development demands of manufacturing, combining with these newly emerging technologies, as shown in Figure 1, a new service-oriented NM mode – cloud manufacturing (CMfg) – was proposed by the authors’ group in 2010 (Li et al. 2010). Based on the authors’ previous work, this article intends to carry on a complete and deep research on the connotation of CMfg, manufacturing cloud service (MCS), manufacturing cloud and CMfg service platform. The remainder of the paper is organised as follows. Section 2 elaborates the concept of CMfg, including the definition, architecture, operational model, typical characteristics of CMfg and the difference with cloud computing. Section 3 studies CMfg service platform and manufacturing cloud. Section 4 presents the prototype of CMfg platform. Section 5 concludes the whole paper. 2. 2.1.

Concept of CMfg The concept of CMfg

Cloud manufacturing (CMfg) is a new manufacturing paradigm based on networks. It uses the network, cloud computing, service computing and manufacturing enabling technologies to transform manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and circulating of

Figure 1.

The proposition of CMfg.

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manufacturing resources and manufacturing capabilities. CMfg can provide safe, reliable, high-quality, cheap and on-demand manufacturing services for the whole life cycle of manufacturing. A CMfg system primarily consists of manufacturing resources and capabilities, manufacturing cloud and the whole manufacturing life cycle applications. It also includes a core support (knowledge), two processes (import and export) and three user types (service providers, cloud operators and service users) as shown in Figure 2 (Li et al. 2010). Manufacturing cloud operators primarily realise efficient management and operation of cloud services, which can provide services for service users according to their requests. Services in the cloud are provided by different providers based on their manufacturing resources and capabilities. Depending on the different manufacturing requirements, a large number of cloud services are collected and integrated to form a manufacturing cloud. The cloud provides the whole manufacturing life cycle process with various manufacturing services. This process is called ‘export’. Service users can use all kinds of dynamic application services in the on-demand way with the supports of the CMfg operation platform and realise the multi-agent collaborative interaction. Knowledge plays a central support role in the running process of CMfg. In a CMfg system, knowledge-based integration across the whole life cycle is possible. Knowledge can not only support virtualisation access and service encapsulation of manufacturing resources and manufacturing capabilities but also can support the

Cloud operators

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The abstract operation principle of CMfg.

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realisation of function based on cloud service such as efficient management, intelligent search, etc.

(1) Resource layer (R-Layer): R-Layer is physical manufacturing resources and capabilities layer. The manufacturing resources include the hard manufacturing resources (e.g. machine tools, machining centres, simulation equipments and test equipments) and the soft manufacturing resources (e.g. computational models, data, software and knowledge in manufacturing process). The manufacturing capabilities are formed with resources, people (or organisation) and knowledge, which reflect the capability to complete a manufacturing task or experiment supported by related manufacturing resources and

Products whole life cycle of service applications (Design, Manufacturing, Management, Maintenance, ...)

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Figure 3.

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The architecture of CMfg.

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Cloud manufacturing standards and criteria \communicaiton \ knowledge\ safety and security

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2.2. The architecture of CMfg The architecture of CMfg is shown in Figure 3, and it consists of the following six layers.

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(2)

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knowledge, including design capability, simulation capability, product capability and other capabilities related to the life cycle of manufacturing process. Perception layer (P-Layer): P-layer is responsible for sensing the physical manufacturing resources and capabilities, enabling them to be connected into the network, and processing the related data and information by using the technologies such as RFID, the Internet of things, etc., to realise the overall connection of various manufacturing resources and capabilities. Service layer (S-Layer): S-Layer contains virtual resources and services of CMfg. It is primarily responsible for the virtualisation and encapsulation of the manufacturing resources and capabilities into related cloud services and then forming the services pool. Middleware layer (M-Layer): M-Layer primarily provides core functions and service supports for the operation of MCSs, such as cloud service management, knowledge management, cooperation management, platform running management, transaction management, failure management, energy management and so on. Application layer (A-Layer): A-Layer is the application layer of CMfgoriented various manufacturing fields and industries, which provides different specific application interfaces and related end-interaction equipments. Different users can access and use the cloud service in CMfg ondemand.

Typical characteristics of CMfg Providing on-demand services for the whole life cycle of manufacturing

CMfg is proposed after cloud computing which provides on-demand IT resources (e.g. server, storage, network and software) for users in the form of service through the Internet, i.e. IaaS, PaaS and SaaS. In CMfg, in addition to the IT resources, all kinds of manufacturing resources and capabilities involved in the whole life cycle of manufacturing are provided to the users, including design as a service (DaaS), manufacturing as a service (MFGaaS), experimentation as a service (EaaS), simulation as a service (SIMaaS), management as a service (MANaaS), maintain as a service (MAaaS), integration as a service (INTaaS), etc. All these services will be used on-demand with the support of a scalable service platform. 2.3.2.

Supporting agile virtual organisation/community

Similar to that the cloud can make the virtual PC with personalised customisation according to the computing requirements of users in cloud computing, the manufacturing cloud can realise the customised VE organisations for the specific manufacturing task requirements. Similar to the user publishing a manufacturing task requirement, the manufacturing cloud can respond promptly, composite manufacturing capabilities and services flexibly and combine all businesses of service providers. In a CMfg platform, users can dynamically construct different virtual organisations or communities according to their own requirements. The platform can provide complete support for all tasks and processes involved in constructing and operating a virtual organisation.

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Intelligent perception of manufacturing resources

CMfg brought all kinds of soft and hard manufacturing resources into a manufacturing cloud, especially for hard manufacturing resource access and perception, such as machine tools and machining centres, the simulation equipment, test equipment, logistics goods and other kinds of manufacturing hard equipment. In CMfg, dynamic states of hard manufacturing resources can be sensed collected with intelligent sensors, bar code, RFID, camera, etc., and data can be transmitted and managed with all kinds of networks including 3G/4G network, satellite nets, sensor nets, the Internet, etc.

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2.3.4.

Knowledge based intelligent manufacturing

Knowledge plays a key role to support a CMfg system. A manufacturing cloud gathers all kinds of manufacturing services together with corresponding knowledge. Along with the evolution of manufacture cloud, the accumulated knowledge scale is expanding continually in the cloud. Knowledge is involved in every phase of both manufacturing processes and the whole life cycle of a cloud service. For a manufacturing process, knowledge provides support to argumentation, design, production, experiment, simulation, management and so on. For a cloud service, knowledge provides support to service description, release, matching, composation, trading, execution, scheduling, settlement, evaluation and so on. 2.3.5.

Wikipedia style and group innovation based manufacturing

Any person, institute or enterprise can participate in and contribute their manufacturing resources, capabilities and knowledge to CMfg service platform. Meanwhile, any enterprise can carry out their manufacturing activities based on these manufacturing resources, capabilities and knowledge. Similar to Wikipedia, CMfg is a group of innovation-based manufacturing model (Tao et al. 2011b). 2.4.

Key technologies for implementing CMfg platform

The key technologies involved in the construction of a CMfg platform can be classified into the following ten classifications: (1) general technologies, (2) CMfg resource perception and access technologies, (3) virtualisation and servitisation technologies of CMfg resource and capability, (4) construction and management technologies of CMfg service environment, (5) CMfg service environment running technologies, (6) CMfg service environment comprehensive evaluation technologies, (7) CMfg safety and security technologies, (8) CMfg pervasive human–computer interaction technologies, (9) informationised manufacturing technology and (10) application implementation technology. 3.

CMfg service platform and manufacturing cloud

3.1. CMfg service platform This paper presents a multi-user oriented, service-based, commercial-available CMfg platform diagram as shown in Figure 4. In this platform, in view of the delivery and usage of manufacturing service, the CMfg users can be classified into two broad categories, i.e. cloud providers side (cloud service provider, CSP) and cloud request side(cloud service demander, CSD). CSP mainly provides the corresponding

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CMfg platform.

manufacturing resource services and manufacturing capability services through network. Through the cloud access technology, resource virtualisation technology, cloud computing technology, etc., the manufacturing resources and capabilities provided by CSP are encapsulated as a CMfg service to form CMfg service pool. CSD requests services through the CMfg platform. According to the task requests submitted to the platform by users, the CMfg services platform searches for the services satisfying the needs of user under the supporting of match and search of services, optimal scheduling, intelligent rent-seeking and other middleware, and then provides services for CSD. In order to further explain the functions of the CMfg platform, an example about market is used to illustrate the role of various actors in a CMfg platform. CMfg platform can be viewed as a market, then CSP can be seen as the suppliers of goods and CSD can be seen as the consumer who buys goods in the market. CMfg platform (the market) attracts different types of CSP (providers of goods) through various effective policies and strategies to provide a wide range of manufacturing services (goods) and integrates a variety of manufacturing services (goods) together to attract CSD (consumers) to use the services in the CMfg platform through effective

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management and marketing manners, so that the tripartite (CMfg platform, CSP and CSD) can all get profit. In the following sections, the three key elements in CMfg, i.e. the manufacturing resource, MCS and manufacturing cloud, are investigated. The constructing method for manufacturing cloud is introduced.

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3.2.

CMfg resources in CMfg service platform

Resources in CMfg can be divided into manufacturing resources and manufacturing capability. Manufacturing resources contain soft resources and hard resources. Soft resources primarily consist of software, data and knowledge, while the hard resources mainly include devices and equipments of manufacturing, experiments, logistics, etc. Manufacturing capabilities are expressed by the combination of manufacturing resources, people (or organisation) and knowledge involved in the manufacturing processes. The classification of manufacturing resources and capabilities is shown in Figure 5. 3.3. MCS in a CMfg service platform Manufacturing cloud service (Zhang et al. 2010), called cloud service for short, is the encapsulated service form of manufacturing resources and manufacturing capabilities. It is the basic element of manufacturing cloud. It can supply applications to users via a network during the whole life cycle of a product development. The forming processes of a MCS are as follows: (a) based on the technologies of Internet of things and virtualisation, the distributed resources are apperceived and connected into the CMfg platform first via network; (b) then the virtual resources pool that can Simulation Capability

Product Capability

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The classification of manufacturing resource and capability.

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be accessed and used on-demand is formed and (c) the virtual resources are encapsulated into MCS and are published and registered into the CMfg platform. Compared with the resource service in traditional NM, a MCS has the characteristics of interoperability, self-organisation and self-adaption, which can provide support for the construction of high-efficiency and intelligent CMfg service platform based on knowledge. The resource servilisation process can also be explained by the example of manufacturing capability servilisation. According to the characteristics and definition of manufacturing capability, the description model of manufacturing capability (DMMC) can be abstractly represented as follows:

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DMMC ¼ ðState=RelationSet; ObjectiveSet; QoSSetÞ where State/relationSet mainly describes MC’s various states and exiting various complex relationship included in the execution process of MC services (MCS); ObjectiveSet is the core part of MC description, mainly including the information about function of MCS, requirement, execution process and other relevant resources; QoSSet is the integration of services quality of MC with all levels, including business-level QoS andservice-level QoS. The ObjectiveSet can be further subdivided and abstracted as follows: ObjectiveSet ¼ ðInternalRelation; StaticAttribute; DynamicBehavior; ServiceQoSÞ In the description model (Figure 6), InternalRelation is the logical relationship among inside elements of MCS and is also the subset of State/relationSet referring to

Manufacturing capability

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The description model of manufacturing capability (DMMC).

evolution rule

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top-level description model of MC. StaticAttribute primarily describes the static attribute information of a MCS, such as basic function information, resources description information, historical cases information, etc., where resources description information is the description of relevant various resources in the process of completing a manufacturing task, such as machine tool, human and team organisation. DynamicBehavior primarily describes the dynamic information in the execution process of a MCS life cycle, such as operation process, completion situation, state transformation, timing interaction, etc. ServiceQoS is the service-level QoS description of a MC and it is the subset of QoSSet in above top-level description model of MC. In addition, in order to provide supports for the formal description and application reasoning of MC description model, ontology base and rule base must be built as shown in Figure 6. The ontology base mainly includes various ontologies (such as field ontology, state ontology, application ontology, etc.) and basic terms of MC (such as concepts, roles and relations), it is the precondition of achieving semantic match of MC and it also provides a description vector for the formal description of MC meta-model. Rule base mainly includes various rules related to application process of MC services, such as transfer rule, reasoning rule, execution rule, assessment rule, evolution rule and so on, and it provides a basis for achieving all kinds of intelligent reasoning. After DMMC is given and the formal description of manufacturing capability is obtained, the suitable service description language is needed to realise the digital description of DMMC and to form the manufacturing capability service. The current common ontology description language includes SOHE, OWL, OWL-S, etc. In CMfg, except for the three kinds of services in cloud computing (i.e. PaaS, IaaS and SaaS), MCS also includes DaaS, MFGaaS, EaaS, SIMaaS, MANaaS, MAaaS and INTaaS. 3.4. Manufacturing cloud in CMfg service platform Manufacturing cloud is the output product of cloud service aggregation according to specific rules and algorithms, which is one of the key points to distinguish traditional NM and CMfg. The traditional NM also encapsulates distributed resources into services, which can be assembled according to some orders to complete complex tasks. But these services are not aggregated for operation and management. In CMfg, a lot of cloud services are aggregated following some rules to become a large cloud service resource pool, i.e. manufacturing cloud, which can supply transparent, open, used on-demand services to users. Manufacturing cloud can be classified into public cloud and private cloud due to the difference of service objects. The public cloud primarily refers to the societyoriented public MCS platform. The sharing and collaboration of manufacturing resources and capabilities among small- and medium-sized enterprises (SMEs) in one area or profession can be improved after CMfg community is formed. Crowd sourcing is encouraged and the competitive power of these enterprises can be enhanced. Manufacturing private cloud mainly refers to the MCS platform constructed inside enterprise or organisation. It can decrease the manufacturing cost and strengthen the innovation design capability of conglomerate by way of realising the sharing and collaboration of various manufacturing resources.

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3.5.

Construction of manufacturing cloud for CMfg service platform

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The construction layers of manufacturing cloud are shown in Figure 7, which mainly described the forming process of manufacturing cloud and the involving key technologies. The six layers included in the construction process are as follows: (1) Resource layer. All kind of resources involved in the whole life cycle of a product are supplied, including manufacturing resources and capabilities, which are classified in detail for better realisation of using different virtual technologies for different resources. (2) Resource–perception layer. The perception devices like two-dimension code labels, RFID labels, readers, GPS and sensors are included in this layer, as well as the resource–perception system, i.e. various adapters supporting soft or hard resources access and the corresponding information process centre. The important issues solved in this layer are resources–perception and identification, collection, classification and aggregation of information, which can support the realisation of intelligent identification and management of resources by manufacturing cloud. (3) Resource virtual access layer. All kinds of distributed resources are accessed to CMfg platform by virtual technologies, and then virtual resources are formed to be aggregated in the virtual resource pool. Then the complexity and dynamics of underlying resources are hidden, which can realise serviceoriented effective share and collaboration of resources in manufacturing cloud platform. (4) Manufacturing cloud core service layer. The core service layer is mainly consist of the three parts: first, cloud services are formed through servilisation

Fault-tolerant Management

Monitoring Management Scheduling Management

Services Description Service register

Service Deployment

Manufacturing Cloud Network Management

User Management

Billing Management

Internet ...

Reflection Management

Virtual resource pool

Telecommunication Network

...

Resource perception system Broadcasting Network Manufacturing resource

Figure 7.

Manufacturing capacity

Productswholelife cycle of service applications

The construction layers of manufacturing cloud.

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operations like encapsulation and publication of virtual resources; then, the corresponding ways of deployment are chosen for different types of cloud services, which are managed intelligently and effectively, such as intelligent matching, dynamic composition, fault-tolerance management, etc.; finally, services in the life cycle of product are supplied to users on-demand, such as scheduling management, change management, cost-count management, etc. (5) Transmission network layer. This layer contains the networks and transmission protocols involved in the whole forming and running process of manufacturing cloud, like three networks – Internet, Broadcast and television network, Telecom network, etc. (6) Terminal application layer. This layer orients the related field and professions of manufacturing industry and provides various service applications during the product life cycle. Users can interact with manufacturing cloud through different terminators, and effectiveness and collaboration among multi-agent tasks are supported. 4.

Prototype of CMfg platform

To verify the proposed cloud construction method and related technology, combining with the existing achievement of research team, making full use of cloud computing, Internet of things and other advanced technology, a design and simulation-oriented cloud service platform prototype is designed and developed by the authors’ group, which mainly focuses on various resources in design and simulation phases during the application of life cycle of a product. The presented cloud service-based platform for sharing and collaboration of resource includes the following four sub-systems: 4.1. Virtualisation-supported subsystem The sensation of various resources (which are in designing and simulation phase) and virtual access (such as customisation, encapsulation, deployment and testing of virtual machine) can be realised, which is composed of resources–perception system module and virtual access system module (as shown in Figure 8). Resources-perception system module mainly realises acquisition, analysis, aggregation and other treatment of resource static properties and resource dynamic properties, and this function module is deployed on the side of providing resource. Virtual access system module primarily realises the mapping from distributed resources to virtual resources. Through taking virtual machine mappings as the accessing carrier, distributed resources are mapped into virtual resources (virtual machine), the operations of individuation customisation, monitoring, fault tolerance and migration of virtual machine mappings are achieved and this function module is mainly achieved through related technologies of Platform. 4.2. Cloud service management subsystem Servitisation registration, distribution of resources, is achieved, and semantic support can be provided to efficient management of cloud services (see Figure 9). During servitisation, standard service description model is first established to achieve

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Figure 8.

Virtualisation support subsystem.

Figure 9.

Cloud service management subsystem.

the formal description of resource services, and then the conditions are provided to realise the services management based on semantic, such as semantic publishing, search and match, services composition, etc. Three function modules are included, i.e. semantic publishing, semantic query and edition of services.

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Knowledge database management subsystem

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Knowledge database is the core part to support the operation of CMfg platform, which is used to achieve the management of domain knowledge, such as extraction,

Figure 10.

Knowledge database management subsystem.

Figure 11.

Cloud services-oriented complex product design subsystem.

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analysis, integration, etc. It takes ontology as knowledge representation, takes logical reasoning engine as the support of knowledge application, and the sharing and reuse of experience and knowledge are realised through constructing case base (see Figure 10). Meantime, this knowledge database system can provide the application interface for other subsystem to realise the seamless connection between knowledge database system and cloud service platform. The main function modules include ontology management, case management, reasoning engine, etc.

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4.4.

Cloud services-oriented complex product design subsystem

This subsystem takes cloud service as a core, provides knowledge management and semantic supporting by knowledge database, provides various application services of life cycle of products to users on-demand through network, thus efficient sharing and collaboration of cloud services are achieved (see Figure 11). The main function module includes semantic search of services, verification of service composition, scheduling, fault tolerance, etc. 5.

Conclusion

Manufacturing service, knowledge-based innovation, the aggregation and collaborative capability of various manufacturing resources, and friendliness to environment have already become the key factors of enterprises competition and for the development trend of manufacturing information (Xu et al. 2006). To seek new development opportunity and market space, improve management level of enterprises and compel manufacturing enterprises to change the development view and traditional manufacturing mode, the authors’ group has presented a new service-oriented manufacturing paradigm – cloud manufacturing. In this paper, a series of issues on CMfg are investigated, including the background, concept, architecture and typical characteristics of CMfg, the key technologies for implementing CMfg and the construction of manufacturing cloud. CMfg will become a typical representative of the advanced manufacturing modes in the next period, whose implementation needs more works under the demand of applications and the development of correlation technologies. The studies and applications of CMfg technologies will further improve the ‘networking, intelligent, and service’ of manufacturing and then promote the manufacturing information to a new level. Acknowledgements This work is supported in part by the 863 Program project in China (No.2011AA040501), the National Science Foundation of China (No.61074144 and No.51005012), the Doctoral Fund of Ministry of Education (NO.20101102110009) and the Fundamental Research Funds for the Central Universities in China. Thanks for the help from the master students Y.J. Laili, D.J. He, Lan Mu, Z.Y. Ren and Y. Bao at the Laboratory of Manufacturing Integration and Simulation Technology (MIST) at Beihang University.

References Abdalla, H.S., 1999. Concurrent engineering for global manufacturing. International Journal of Production Economics, 60–61, 251–260.

Downloaded by [Beihang University] at 01:30 15 September 2014

Enterprise Information Systems

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Chen, K. and Zheng, W.M., 2009. Cloud computing: system instances and current research. Journal of Software, 20 (5), 1337–1348. Chen, X.X., He, Y.L., and Huang, H., 2011. An approach to automatic development of interlocking logic based on Statechart. Enterprise Information Systems, 5 (3), 273–286. Dan, B., et al., 2005. Network-integrated manufacturing system. International Journal of Production Research, 43 (12), 2631–2647. Feng, S., Li, L.X., and Cen, L., 2001. An objected-oriented intelligent design tool to aid the design of manufacturing systems. Knowledge-Based Systems, 14 (5-6), 225– 232. Feng, S., et al., 2003. Using MLP networks to design a production scheduling system. Computers & Operations Research, 30, 821–832. Flammia, G., 2001. Application service providers: challenges and opportunities. IEEE Intelligent Systems and Their Applications, 16 (1), 22–23. Kalpakjian, S. and Schmid, S., 2006. Manufacturing engineering and technology. 5th ed. New Jersey: Prentice Hall, 1192. ISBN 9787302125358. Kazuaki, I., et al., 1997. Virtual manufacturing systems as advanced information infrastructure for integrating manufacturing resources and activities. CIRP Annals – Manufacturing Technology, 46 (1), 335–338. Kumar, S., Kadow, B.B., and Lamkin, M.K., 2011. Challenges with the introduction of radiofrequency identification systems into a manufacturer’s supply chain-a pilot study. Enterprise Information Systems, 5 (2), 235–253. Laplante, P.A., 2005. Comprehensive dictionary of electrical engineering. 2nd ed. USA: CRC Press, 136. ISBN 9780849330865. Li, B.H., Chai, X.D., and Hou, B.C., 2009. Networked modeling & simulation platform based on concept of cloud computing—cloud simulation platform. Journal of System Simulation, 21 (17), 5292–5299 (in Chinese). Li, B.H., et al., 2010. Cloud manufacturing: a new service-oriented manufacturing model. Computer Integrated Manufacturing Systems, 16 (1), 1-8 (in Chinese). Li, H., et al., 2000. A production rescheduling expert simulation system. European Journal of Operational Research, 124 (2), 64–74. Li, H.Z. and Li, L.X., 2000. Integrating systems concepts into manufacturing information systems. Systems Research and Behavioral Sciences, 17 (2), 135–147. Li, L., 2007. Supply chain management: concepts, techniques, and practices – enhancing value through collaboration. Hackensack, NJ: World Scientific Publishing, 372. ISBN 13 9789812700728. Meier, H., Volker, O., and Funke, B., 2010. Industrial Product-Service Systems (IPS2) – paradigm shift by mutually determined products and services. International Journal of Advanced Manufacturing Technology, 52 (9-12), 1175–1191. Montreuil, B., Frayret, J.-M., and D’Amours, S., 2000. A strategic framework for networked manufacturing. Computers in Industry, 42 (2-3), 299–317. Newman, S.T., Nassehi, A., and Xu, X.W., 2008. Strategic advantages of interoperability for global manufacturing using CNC technology. Robotics and Computer-Integrated Manufacturing, 24 (6), 699–708. Ning, H.S., Zhang, Y., Liu, F.L., 2006. Research on China Internet of things’ services and management. Acta Electronica Sinica, 34 (12), 2514–2517. Park, K.H. and Favrel, J., 1999. Virtual enterprise-information system and networking solution. Computers & Industrial Engineering, 37, 441–444. Preiss, K., 1994. Agile manufacturing. Computer-Aided Design, 26 (2), 83–84. Smith, A.D. and Rupp, W.T., 2002. Application service providers (ASP): moving downstream to enhance competitive advantage. Information Management and Computer Security, 10 (2–3), 64–72. Tan, W.A., et al., 2008. A business process intelligence system for enterprise process performance management. IEEE Transactions on SMC Part C, 38 (6), 745–756. Tan, W.N., et al., 2010. A methodology toward manufacturing grid-based virtual enterprise operation platform. Enterprise Information Systems, 4 (3), 283–309. Tao, F., Hu, Y.F., and Zhang, L., 2010a. Theory and practice: optimal resource service allocation in manufacturing grid. Beijing, China: China Machine Press, 239. ISBN 978-7111-30313-8.

Downloaded by [Beihang University] at 01:30 15 September 2014

186

L. Zhang et al.

Tao, F., Hu, Y.F., and Zhao, D., 2009b. Study on manufacturing grid resource service modeling and evaluation. International Journal of Advanced Manufacturing Technology, 41 (9–10), 1034–1042. Tao, F., Hu, Y.F., and Zhou, Z.D., 2008a. Study on manufacturing grid and its resource service optimal-selection system. International Journal of Advanced Manufacturing Technology, 37 (9-10), 1022–1041. Tao, F., Hu, Y.F., and Zhou, Z.D., 2009c. Application and modeling of resource service trustqos evaluation in manufacturing grid system. International Journal of Production Research, 47 (6), 1521–1550. Tao, F., Zhang, L., and Hu, Y.F., 2011d. Resource services management in manufacturing grid system. USA: Wiley, Scrivener Publishing, 550. ISBN 978-1-118-12231-0. Tao, F., Zhang, L., and Nee, A.Y.C., 2011c. A review of the application of grid technology in manufacturing. International Journal of Production Research, 49 (13), 4119–4155. Tao, F., Zhao, D., and Zhang, L., 2010c. Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowledge and Information Systems, 25 (1), 185–208. Tao, F., et al., 2008b. Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Transactions on Industrial Informatics, 4 (4), 315–327. Tao, F., et al., 2009a. Study on resource service match and search in manufacturing grid system. International Journal of Advanced Manufacturing Technology, 43 (3-4), 379–399. Tao, F., et al., 2010b. Study of failure detection and recovery in manufacturing grid resource service scheduling. International Journal of Production Research, 48 (1), 69–94. Tao, F., et al., 2011a. Typical characteristics of cloud manufacturing and several key issues of cloud service composition. Computer Integrated Manufacturing Systems, 17 (3), 477–486 (in Chinese). Tao, F., et al., 2011b. Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, 225 (10), 1969–1976. Ulieru, M., et al., 2000. A multi-resolution collaborative architecture for web-centric global manufacturing. Information Sciences, 127 (1-2), 3–21. Valle, S. and Va´zquez-Bustelo, D., 2009. Concurrent engineering performance: incremental versus radical innovation. International Journal of Production Economics, 119 (1), 136– 148. Wang, C.G. and Xu, L.D., 2008. Parameter mapping and data transformation for engineering application integration. Information Systems Frontiers, 10 (5), 589–600. Wang, C.G., Xu, L.D., and Peng, W.L., 2007. Conceptual design of remote monitoring and fault diagnosis systems. Information Systems, 32 (7), 996–1004. Wang, K., et al., 2010. A service-based framework for pharmacogenomics data integration. Enterprise Information Systems, 4 (3), 225–245. Wang, L., Zeng, J.W., and Xu, L., 2011. A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. Information Technology & Management, 12 (2): 111–119. Xu, L., et al., 2005. A polychromatic sets approach to the conceptual design of machine tools. International Journal of Production Research, 43 (12), 2397–2422. Xu, L., et al., 2007. A decision support system for product design in concurrent engineering. Decision Support Systems, 42 (4), 2029–2042. Xu, L.D., 2011a. Information architecture for supply chain quality management. International Journal of Production Research, 49 (1), 183–198. Xu, L.D., 2011b. Enterprise systems: state of the art and future trends. IEEE Transactions on Industrial Informatics, 7 (4), 630–640. Xu, L.D., et al., 2006. Integrating knowledge management and ERP in enterprise information systems. Systems Research and Behavioral Science, 23 (2), 147–156. Xu, L.D., et al., 2008. An approach to enterprise process dynamic modeling supporting enterprise Process evolution. Information Systems Frontiers, 10 (5), 611–624. Xu, S.B. and Xu, L., 2011. Management: a scientific discipline for humanity. Information Technology & Management, 12 (2), 51–54.

Enterprise Information Systems

187

Downloaded by [Beihang University] at 01:30 15 September 2014

Xu, W., Wei, Y., and Fan, Y., 2002. Virtual enterprise and its intelligence management. Computers & Industrial Engineering, 42, 199–205. Yin, Y.H. and Xie, J.Y., 2011. Reconfigurable manufacturing execution system for pipe cutting. Enterprise Information Systems, 5 (3), 287–299. Yusuf, Y.Y., Sarhadi, M., and Gunasekaran, A., 1999. Agile manufacturing: the drivers, concepts and attributes. International Journal of Production Economics, 62 (1-2), 33–43. Zhang, L., et al., 2010. Study on the key technologies for the construction of manufacturing cloud. Computer Integrated Manufacturing Systems, 16 (11), 2510–2520 (in Chinese). Zhang, Y., et al., 2011. A new method for automatic synthesis of tolerances for complex assemblies based on polychromatic sets. Enterprise Information Systems, 5 (3), 337–358.