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A platform for optimisation in distributedmanufacturing enterprises based on cloudmanufacturing paradigmOmid Fatahi Valilaia & Mahmoud Houshmanda

a Advanced Manufacturing Laboratory, Department of Industrial Engineering, SharifUniversity of Technology, Tehran, IranPublished online: 05 Feb 2014.

To cite this article: Omid Fatahi Valilai & Mahmoud Houshmand (2014) A platform for optimisation in distributedmanufacturing enterprises based on cloud manufacturing paradigm, International Journal of Computer IntegratedManufacturing, 27:11, 1031-1054, DOI: 10.1080/0951192X.2013.874582

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A platform for optimisation in distributed manufacturing enterprises based on cloudmanufacturing paradigm

Omid Fatahi Valilai* and Mahmoud Houshmand

Advanced Manufacturing Laboratory, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

(Received 17 January 2013; accepted 30 October 2013)

The synergy of fundamental factors like changes in governmental policies, global expansions of the manufacturingindustries and improvements in technology related to reliability of manufacturing information flow have created the globalmanufacturing revolution in the first years of the twenty-first century. Although the concept of global manufacturingenterprise has created benefits and opportunities for today’s manufacturing enterprises, it has enforced enterprises to endurecosts to adopt the global manufacturing solutions. Besides, considering the researches aimed to enable the globalmanufacturing paradigm, there is still a need for efficient solutions to fulfil the global product development requirements.In this paper, the essential aspects and requirements of a global manufacturing approach are studied. Considering theessential requirements such as integration of manufacturing operations over the globe, maintaining manufacturing networksin distributed manufacturing enterprises, collaboration of global manufacturing agents within the networks and enabling theoptimal solution systems in global manufacturing enterprises, the paper reviews the dominant researches for the globalmanufacturing solutions. Considering the new paradigm for global manufacturing called cloud manufacturing discussed inthe latest works of the authors, the paper proposes a new manufacturing platform called STRATUS Cloud for today’s globalmanufacturing. The architecture of STRATUS Cloud is discussed based on the cloud manufacturing paradigm to fulfil therequirements of global manufacturing platform especially in enabling the global manufacturing for adoption of optimalsolution systems. The STRATUS Cloud inherits capabilities from the recent authors’ researches such as collaborationsupport of distributed manufacturing agents over the globe and the integration of the manufacturing operation based on theSTEP standard while extending its application and fulfilling STEP’s limitations. The STRATUS Cloud proposes acontribution for enabling the adoption of the optimal solutions through manufacturing processes. This contribution isachieved via a manufacturing cloud reconciled with the proposed platform structure. The capabilities of the STRATUSCloud to enable optimisation systems are discussed in different case study scenarios.

Keywords: global manufacturing; e-manufacturing; optimisation systems; collaborative manufacturing; distributed enter-prises; computer integrated manufacturing (CIM); cloud manufacturing paradigm

Introduction

The synergy of fundamental factors such as changes ingovernmental policies, global expansions of the manufac-turing industries and improvements in technology relatedto reliability of manufacturing information flow havecreated the global manufacturing revolution in the firstyears of the twenty-first century (O’Brienn 2002; Praterand Ghosh 2006; Tu et al. 2006; West and Bengtsson2007; Chryssolouris, Papakostas, and Mavrikios 2008;Koren 2010). The concept of global manufacturing enter-prise has created benefits and opportunities such asreduction of manufacturing costs by utilising lowlabour-cost countries (Kotha 1996; Koren 2010; Tu andDean 2011), reduction of business risks (Paolucci andSacile 2005; Galan et al. 2007; Abouel Nasr andKamrani 2007; ElMaraghy and Wiendahl 2009; Wu andLiu 2009) and introduction of new markets as a newsource for enterprise growth (Dangayach and Deshmukh2006; Lu and Storch 2011; Abdelkafi et al. 2011; Tu andDean 2011).

However, global manufacturing has faced enterpriseswith challenges such as integration of manufacturingoperations in different countries over the globe toachieve manufacturing efficiency (Feng and Wu 2009;Houshmand and Valilai 2012), evolving of a new structureand configuration of networks for distributed manufactur-ing enterprises (Manuj and Mentzer 2008; Yoo andKumara 2010; Valilai and Houshmand 2010b; Liu,Young, and Ding 2011), enabling collaboration betweenproduction centres and manufacturing networks for com-petitive advantage (Shi 2003; Rodriguez Monroy and Arto2010) and adoption of solutions to respond to the chan-ging functional requirements of products in global distrib-uted manufacturing enterprises in short period of time withcost-effective approaches (Hallgren and Olhager 2006;Hwang and Katayama 2009; Flowers and Cheng 2011).Moreover, global manufacturing is also willing for solu-tions to overcome the resulted global problems such asgreen product design and sustainable manufacturing thataims to improve the energy efficiency of manufacturing

*Corresponding author. Email: [email protected]

International Journal of Computer Integrated Manufacturing, 2014Vol. 27, No. 11, 1031–1054, http://dx.doi.org/10.1080/0951192X.2013.874582

© 2014 Taylor & Francis

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mailto:[email protected]

products and processes to overcome manufacturing envir-onmental impacts (Kara, Manmeka, and Herrmann 2010;Luh, Chu, and Pan 2010).

To enable the global manufacturing enterprises to facethe aforementioned challenges in global product develop-ment, many researches have been initiated. However, thereis still a considerable gap between the global productdevelopment requirements and the solutions efficiency tofulfil them (Paolucci and Sacile 2005; Tseng, Kao, andHuang 2008, Terkaj, Tolio, and Valente 2009; Pritschowet al. 2009; ElMaraghy and Meselhy 2009; Hesmer, Duinand Thoben 2011; Xu 2012). In the next section, the paperstudies the essential aspects and requirements of a globalmanufacturing approach. Defining the essential require-ments, in section ‘Overview of current researches forglobal manufacturing approaches’, the paper reviews therelated researches and solutions offered as an enablerapproach in global manufacturing environment. In section‘STRATUS Cloud; ’, the paper proposes a novel manu-facturing platform. The proposed platform structure isbased on a service-oriented approach of cloud manufactur-ing paradigm. The cloud manufacturing paradigm andservice-oriented approach are discussed in the sections‘Cloud manufacturing paradigm’ and ‘STRATUS Cloudservice-oriented solution’. In the section ‘STRATUSCloud architecture’, the paper describes the architectureof proposed platform for STRATUS Cloud. The differentaspects of this idea will be discussed in detail, and thecapabilities of STRATUS Cloud to satisfy the require-ments of a global manufacturing approach are discussed.And finally in the section ‘Case study scenarios’, a briefcase study is described to show and verify the proposedidea capabilities to enable adoption of the optimisationsystems.

Essential requirements of a global manufacturingapproach

In this section, the paper describes the essential require-ments to develop a platform for global manufacturingsystems. These requirements are proposed as follows.

Integration of manufacturing operations over the globeto achieve manufacturing efficiency

One of the essential requirements to approach the globalmanufacturing paradigm is related to digital manufactur-ing and computer-integrated manufacturing systems(Rudberg and Olhager 2003; Paolucci and Sacile 2005;Zhou, Qiu, et al. 2007; Tseng, Kao, and Huang 2008;Terkaj, Tolio, and Valente 2009; Pritschow et al. 2009;ElMaraghy and Meselhy 2009; Hesmer, Duin and Thoben2011; Zhang et al. 2011; Xu 2012). The integration ofmanufacturing operations through enterprises is proposedto be essential for increasing the competitiveness and the

ability of the enterprises for a time-efficient response todemand fluctuations in the market (Patel et al. 2006; Fengand Wu 2009; Wang, Chan, and Pauleen 2010). Thisintegration is limited by the technology limitation of cur-rent manufacturing systems (Hwang and Katayama 2009;Lebreton, Van Wassenhove, and Bloemen 2010). Theintegration of manufacturing processes is discussed in awide range of topics such as integrating engineeringdesign databases and product information (Jiao andHelander 2006), integration of business activities (Liu,Young, and Ding 2011), supply chain integration(Molina, Velandia, and Galeano 2007; Bachlaus et al.2008; Wang, Chan, and Pauleen 2010) and integrationof manufacturing operations information analysis(Hernandez-Matias et al. 2008).

The lack of interoperability between information sys-tems is becoming more and more a major problem in thecollaboration of enterprises in global product developmentprocesses (Kosanke 2005; Martin 2005). During the pro-duct development process, the collaborating agents haveto exchange product information and should have thesame understanding of the exchanged information and totrust both the communication and validity informationcontents (Kosanke 2005; Cutting-Decelle et al. 2007).Today’s manufacturing businesses are moving towardsglobally disseminated and collaborative companies toremain competitive. It is therefore essential that informa-tion and knowledge sharing systems support the globalnature of business interactions (Young et al. 2005;Cutting-Decelle et al. 2007).

To provide common models to define a basis for dataexchange and sharing, one of the best efforts is the workof the ISO in the ISO TC 184/SC4 committee in the lasttwo decades (Carnahan et al. 2005; Cutting-Decelle et al.2007; Kramer and Xu 2009; Valilai and Houshmand2010b). One of the major aspects of the researches toenable integration of manufacturing operations is relatedto the introduction of product information model (Ray andJones 2006; Zhou, Xi, et al. 2007; Panetto and Molina2008; Zhao, Habeeb, and Xu 2009; Jiang, Peng, and Liu2010; Houshmand and Valilai 2013). Of these researchesis the STEP standard – ISO 10303 – which has beendeveloped and is improving under auspices of theInternational Standard Organization and is believed to beone of the most successful solutions (Liang and O’Grady1998; Xu and Newman 2006; Zhao and Liu 2008; Wanget al. 2009; Valilai and Houshmand 2011; Panetto,Dassisti, and Tursi 2012). However, the adoption ofSTEP standard has encountered many problems (Pratt,Anderson, and Rangerc 2005; Lee, Eastman, and Sacks2007; Ball, Ding, and Patel 2008; Gielingh 2008).Different researches have been conducted to propose anefficient approach to overcome these limitations (Ma,Chen, and Thimm 2009; Nylund and Andersson 2010;Valilai and Houshmand 2010b; Houshmand and Valilai

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2012). Considering the well-architected structure of STEPstandard and its continuous improvement under the super-vision of ISO TC 184/SC4 committee besides the vastresearches conducted to fulfil the STEP standard limita-tions, this paper considers the concept of manufacturingoperation integration as using ISO 10303 standard forintegrating manufacturing data.

Support of manufacturing networks in distributedmanufacturing enterprises

In the global manufacturing paradigm, the new forms oforganisational structures in manufacturing enterprises arebuilt up to enable distribution of manufacturing agents informs such as extended enterprise, virtual enterprise, vir-tual organisation, supply chain management and enter-prise clusters (Nylund and Andersson 2010; Wu 2010;Mikos et al. 2011). These organisational forms try toenable the enterprises to use distributed resources effec-tively and also to manage the manufacturing processinformation among the distributed agents in global man-ufacturing environment (Aziz et al. 2005; Panchal et al.2007; Colombo and Harrison 2008; Nylund andAndersson 2010; Jinl, Janamanchi, and Feng 2011;Mikos et al. 2011).

Of the approaches proposed for distributed agents,distributed net cooperation for manufacturing systems ismostly used to enable flexibility and quick response (Guoand Zhang 2009; Vancza et al. 2011). These networksenable the manufacturing enterprises engaged in globalmanufacturing to link to partnering companies throughthe globe and benefits from the various activities such asmanufacturing research and development, product design,free production line capacities and even supporting pro-cesses such as marketing (Tso, Lau, and Ho 2000; Lal andOnwubolu 2007; Manuj and Mentzer 2008; Chen andWang 2009; Liu, Young, and Ding 2011). This paperstudies the concept of distributed manufacturing agentssupport over the globe in dominant researches.

Supporting collaboration in global manufacturingnetworks

The global manufacturing environment requires a colla-borative approach through the manufacturing enterprisefacilitates, the agility in quick response to changes(Wang and Zhang 2002; Mahesh, Ong, and Nee 2007;Chae, Choi, and Kim 2007) and expanding the manufac-turing capacity for a quick and effective response to cus-tomer needs (Rouibah and Ould-Ali 2007; Cheng, Ye, andYang 2009). Enabling the collaboration in manufacturingenterprises is proposed to facilitate achievement of effec-tive supply chain operation (Wang, Chan, and Pauleen2010) and product cycle time reductions in manufacturingnetworks (Zhou and Li 2005; Molina, Velandia, and

Galeano 2007). The research trends for today’s enterprisesto propose different strategies in supply chain manage-ment as a competitive strategy in global manufacturingimplied that all companies in the extended enterprise musteffectively collaborate during the manufacturing processesin different phases (Halevi 2001; Helo et al. 2010). Thispaper discusses the dominant researches for their ability toenable the collaboration among manufacturing agents intheir manufacturing operations and proposes procedures tofulfil this requirement.

Enabling the adoption of optimal solutions in globalmanufacturing enterprises

Considering factors such as the variety of alternativeresource, uncertainties in manufacturing costs and risksthrough collaboration with other manufacturing partners,a challenge of today’s manufacturing enterprises in globalmanufacturing is concerned making the effective andoptimal decisions through the manufacturing operations(Biswas and Narahari 2004; Akanle and Zhang 2008;Hammami, Frein, and Hadj-Alouane 2009). The manu-facturing operation management for optimal decisionmaking should consider factors related to products, man-ufacturing facilities, processes, planning and control sys-tems (Goh, Lim, and Meng 2007; Bozarth et al. 2009; Li,Sheng, et al. 2010). These factors boost the process ofdecision making due to the complexities such as thenumber of supported parts and products, the types ofmanufacturing processes and the stability of manufactur-ing schedules from one period to the next. Manufacturingstrategy of companies under these circumstances shouldbe modified in accordance with the changing factors inglobal manufacturing environment (Choi and Chan 2003;Chen and Wang 2009).

The global manufacturing enterprises essentiallyrequire enablers to improve the way they run their overalloperations in manufacturing operations in the areas suchas capacity management from a strategic point of view(Ng and Jiao 2004), planning the development of newprocesses (Lee and Wilhelm 2010), scheduling the activ-ities and development processes, deploying the propercapacities of processes in operations (Ko, Tiwari, andMehnen 2010), decision-making process in outsourcingcases (Ip et al. 2003; Jiao and Helander 2006) and man-agement of the value chain and linking the manufacturingresources to achieve the optimal performance (Dekkers2003). Therefore, the efficient and optimal solution forglobal manufacturing enterprises forces the enterprises toimprove their performance in different manufacturing pro-cesses via various strategic and operational tools (Talluriand Baker 2002; Vancza et al. 2011). In this paper, theauthors discuss the different approaches proposed byresearchers as enablers of optimum solution adoption inglobal manufacturing environments.

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Overview of current researches for globalmanufacturing approaches

Different research works have been conducted to clarifythe benefits of global manufacturing in competitiveenvironment. These works propose solutions and sys-tematic approaches to enable the implementation ofsolutions in global manufacturing enterprises. Havingdefined the essential requirements to approach globalmanufacturing paradigm in the previous section, thispaper reviews the recent researches and solutionsdetailed in Table 1. Some of them are curtly discussedbelow.

Nassehi, Newman, and Allen (2006) proposed aplatform for interoperability of CAD/CAM/CNCmachining systems. This platform uses ISO 10303(STEP) standard to integrate the CAD/CAM/CNCmachining chain. This platform lacked structure andprocedures to support distributed manufacturing agents,nor enabling the adoption of optimal solutions. Newmanand Nassehi (2007), Valilai and Houshmand (2010b)and (2013) and Houshmand and Valilai (2012) proposedplatforms that enable collaboration among CAD/CAM/CNC machining systems. Also, Qiu and Xu (2009)proposed an approach for CAD/CAM manufacturing

Table 1. Current researches of global manufacturing approaches.

Researchers

STEP-basedintegration ofmanufacturingoperations

Scope of collaboration supportin global manufacturing

networks

Support ofdistributed

manufacturingenterprises

Enabling theadoption ofoptimalsolutions

Nassehi, Newman, and Allen (2006) Yes CAD/CAM/CNC No NoNewman and Nassehi (2007) Yes CAD/CAM/CNC Yes NoHoushmand and Valilai (2012) Yes CAD/CAM/CNC No NoValilai and Houshmand (2010a) Yes NPD/CAD/CAM/CNC Yes NoValila, Jafari-Nodeh, and Houshmand (2010) Yes A-RMS/CAD/CAM/CNC Yes NoQiu and Xu (2009) Yes CAD/CAM Yes NoValilai and Houshmand (2010b) Yes CAD/CAM/CNC Yes NoValilai and Houshmand (2013) Yes CAD/CAM/CNC Yes NoDong et al. (2008) Yes CAD/CAM/CNC/PPRC Yes NoAmeziane (2000) Yes CAD Yes NoBock et al. (2010) No CAD No NoDevaraj, Hollingworth, and Schroeder (2004) No MSM No NoReinsch et al. (2003) No A-PL/PPRC No NoNylund and Andersson (2010) No PPRC Yes NoMikos et al. (2011) No A-PL/PPRC Yes NoLin and Long (2011) No A-PL Yes NoRao et al. (2006) No PPRC Yes NoPappas et al. (2006) No NPD/A-PL/PPRC Yes NoWang (2011) No CAM/PPRC Yes NoOztemel and Tekez (2009) No NPD/CAD/CAM/PPRC/QC-M Yes NoFeldmann and Rottbauer (2000) No A-PL Yes NoLal and Onwubolu (2007) No CAM/CNC Yes NoDekkers (2003) No MSM No YesDemeter (2008) No MSM No YesDurieux and Pierreval (2004) No MHS No YesHallgren and Olhager (2006) No MSM No YesHernandez-Matias et al. (2008) No MSM/QC-M/A-PL No YesKenne, Boukas, and Gharbi (2003) No A-PL/PMC No YesKoren and Shpitalni (2010) No MSM/A-PL No YesChituc, Azevedo, and Toscano (2009) No MSM/SCM Yes YesChen and Wang (2009) No L&D Yes YesJiao and Helander (2006) No SCM Yes YesGuo and Zhang (2009) No A-PL/PPRC Yes YesGuo and Zhang (2010) No A-PL/PPRC Yes YesHarrison et al. (2006) No A-RMS Yes YesRodriguez Monroy and Arto (2010) No MSM Yes YesAkanle and Zhang (2008) No SCM Yes Yes

Notes: A-PL, assembly line/production line control; CAD, computer-aided design; CAM, computer-aided manufacturing; CNC, CNC machining; L&D,logistic and dispatching; MSM, manufacturing strategy management/planning; MHS, material handling system; NPD, new product development; PMC,preventive maintenance/control; PPRC, production planning and resource control; QC-M, quality control/management; A-RMS, computer systems forautomated/robotic operations/control; SCM, supply chain management/planning.


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systems. These platforms integrate the product databased on the STEP standard and also enable the dis-tributed manufacturing agents to collaborate with eachother. However, these approaches lack the structure andprocedures that enable the adoption of optimal solutionby using the manufacturing data.

To extend the scope of collaboration support, Valilaiand Houshmand (2010a) proposed a new approach tosupport the NPD (New Product Development) processesin manufacturing environments. Further, Valilai, Jafari-Nodeh, and Houshmand (2010) developed the new proce-dures to enable the collaboration in programming therobot movements during CNC machining operations inmanufacturing processes. Moreover, Dong et al. (2008)proposed structure and procedures to use web-basedextended manufacturing resources. These researches ben-efit from integration based on the STEP standard andenable the distributed manufacturing agents for productdevelopment collaboration. However, they lacked the pro-cedures that enable the adoption of optimal solutions byprocessing the manufacturing data.

Reinsch et al. (2003) conducted a research onadvanced manufacturing systems for forging productsseeking a high-level logistical performance to meet thecustomers’ requirements for a flexible and reliable in-time delivery with short lead times. Devaraj,Hollingworth, and Schroeder (2004) proposed the idea ofsignificant effects of generic manufacturing strategies(GMS) alignment with manufacturing objectives on rele-vant plant-level performance outcomes. Bock et al. (2010)conducted researches on a product modelling languagethat benefits from ontology and expanded capabilities ofconventional product modelling languages. The researchessupport the collaborative manufacturing operations anddata integration, but not ISO-based integration. There areno structure and procedures to enable the distributedagents’ collaboration, nor to support the optimal solutionadoption included in the proposed approach.

Rao et al. (2006) proposed architecture for buildingup agile collaborative manufacturing systems for diversemanufacturing resources with heterogeneous and physi-cally distributed characterisation. Pappas et al. (2006)conducted a research on a web-based collaboration plat-form for manufacturing product and process design eva-luation using virtual reality techniques. Wang (2011)worked on an integrated approach for developing aweb-based system, including enhanced adaptability, dis-tributed process planning, real-time monitoring andremote machining. Mikos et al. (2011) proposed a sys-tem for distributed knowledge sharing and reuse in thePFMEA (Potential Failure Modes and Effects Analysisin Manufacturing and Assembly Processes) domain bymeans of an ontology that enables knowledge inferenceand retrieval in manufacturing environments with dis-tributed resources. The approaches enable a

collaborative environment for distributed manufacturingsystems, but they do not support integration based onISO-based standards, nor enabling the adoption of opti-mal solutions.

Dekkers (2003) conducted a study on the effect ofdecisions on outsourcing and acquisition of resources.With a similar aim, Demeter (2008) proposed a modelbased on the hypothesis that the existence of manufactur-ing strategy affects the company-level competitiveness.Kenne, Boukas, and Gharbi (2003) proposed an approachfor analysis of the optimal production control and correc-tive maintenance planning for a failure-prone manufactur-ing system. Durieux and Pierreval (2004) conducted aresearch on the sensitivity analysis to improve the designof automated systems. Hallgren and Olhager (2006) inves-tigated the various aspects of quantification in manufac-turing strategy-related issues and proposed a frameworkand methodology for quantitative modelling of manufac-turing strategy. Hernandez-Matias et al. (2008) proposedan integrated modelling framework with the capability ofmanufacturing system analysis that can increase the capa-city of modelling tools for the creation of structureddatabase. Koren and Shpitalni (2010) proposed a newapproach for the design of the reconfigurable manufactur-ing systems that benefit from the advantages both ofdedicated lines and flexible systems. The aforementionedapproaches provide the manufacturing enterprises to adoptthe optimal solutions in the area of manufacturing strategymanagement. However, these approaches do not supportthe ISO-based integration of manufacturing data, nor sup-porting the distributed enterprises to achieve the manufac-turing strategies through their distributed sections over theglobe.

Jiao and Helander (2006) proposed the developmentof a platform for customised product development overthe Internet. Harrison et al. (2006) studied the concept ofcollaboration for coordination of the different engineer-ing operations in a distributed manufacturing environ-ment over the globe. Akanle and Zhang (2008) proposeda methodology to optimise supply chain configurationsto fulfil the customer demand over a period of time. Guoand Zhang (2009, 2010) proposed an architecture con-sisting of various autonomous agents that are capable ofcommunicating with each other to enable decision-making processes based on their knowledge. RodriguezMonroy and Arto (2010) proposed a conceptual frame-work that permits a sequential analysis of factors thataffect the design of a global manufacturing virtual net-work, such as its strategy, structure, communication sys-tems and network culture and their dependence andinfluence on each other. These platforms help the enter-prises for adoption of optimal solutions. In addition, theysupport the distributed manufacturing enterprises.However, they do not consider the ISO-basedintegration.


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Proceeding research

Considering the current global manufacturing enablerapproaches as shown in Table 1, the first group hasfocused on manufacturing product data integration basedon STEP standard, although most of these approachessupport distributed manufacturing enterprises but nonehas the structure and procedures to support the adoptionof optimal solutions. These approaches impose a limita-tion on the manufacturing data integration in the scope ofproduct data structures based on the STEP standard datastructure. This limitation restricts the application of datafor manufacturing operation scopes other than CAD/CAPP/CAM/CNC machining operations. The secondgroup of the enabler approaches concentrates on a specificmanufacturing operation scope. These enabler approachesdo not support distributed manufacturing enterprises, norsupport the adoption of optimal solution algorithmsthrough manufacturing data. These enabler approacheslack a consistent and extendable integration approach formanufacturing data. The third group of the enablerapproaches has extended the second group for supportingmanufacturing enterprises that are distributed over theglobe. However, these approaches did not improve thelimitations of the second group for product data integra-tion based on ISO standards, nor supporting the adoptionof optimal solutions algorithms.

The fourth group of enabler approaches has concen-trated on solutions to enable the manufacturing enterpriseto adopt optimal solutions. These optimal solutions areachieved through processing of manufacturing data.However, none of these approaches support manufacturingdata integration based on ISO standards, nor support thedistributed manufacturing enterprises. The fifth group ofenabler approaches discussed in this paper has extendedthe idea of the fourth group for supporting the extendedmanufacturing enterprises. These enabler approachesenable the adoption of the optimal solutions while themanufacturing enterprises have organisational agents orsuppliers that are distributed over the globe. However,these approaches still lack the structure and proceduresthat integrate the manufacturing data based on ISOstandards.

In what follows, the paper proposes a new and extend-able manufacturing platform for today’s global manufac-turing enterprises. This idea is based on a new paradigmfor global manufacturing called cloud manufacturing.Moreover, considering the aforementioned researches,the paper proposes its contribution by adapting the recentworks of authors that is a distributed, collaborative andintegrated platform called XMLAYMOD (Valilai andHoushmand 2013). The platform adopts the concept ofthe service-oriented structures in cloud manufacturingparadigm. The different aspects of this contribution willbe described in the rest of the paper. Different case study

scenarios for different manufacturing operations have beendiscussed to ensure the capabilities of the proposed man-ufacturing platform.

STRATUS Cloud: a collaborative and integratedplatform to enable optimisation systems in distributedmanufacturing enterprises based on cloudmanufacturing paradigm

Cloud manufacturing paradigm

Cloud manufacturing, known as a movement from pro-duction-oriented manufacturing to service-oriented manu-facturing (Xu 2012), is a relatively new concept. Cloudmanufacturing offers a proper solution in which distribu-ted manufacturing resources are encapsulated into cloudservices and managed in a centralised way (Tao, Hu, andZhang 2010; Shen et al. 2011; Valilai and Houshmand2013). In the cloud manufacturing paradigm, manufactur-ing agents are supported to use cloud services based ontheir manufacturing operation requirements such as cloudcomputing paradigm (Li, Zhang, et al. 2010). In the con-text of cloud computing, paradigm everything is treated asa service (Goscinski and Brock 2010; Bohm and Kanne2011; Subashini and Kavitha 2011; Xu 2012). The rangeof these services varies from product design, manufactur-ing, testing, management or any other operation require-ment in the product lifecycle manufacturing operations(Cheng and Zhu 2011; Subashini and Kavitha 2011;Zhan et al. 2011; Maurera et al. 2012).

The cloud manufacturing has two types of manufac-turing resources (Xu 2012) known as manufacturing phy-sical resources such as equipment, computers, servers andraw materials that are usually tangible. The second type ofmanufacturing resources is manufacturing capabilities thatare intangible such as different enterprises’ applicationsoftware, data mining and data analysis methodologies,standards, manufacturing experience in different manufac-turing operation (Fan and Xiao 2011; Yin et al. 2011).

STRATUS Cloud Service-oriented solution

The STRATUS Cloud framework has a service-orientedarchitecture. As mentioned earlier, STRATUS benefitsfrom the latest work of the authors called XMLAYMOD.The architecture of XMLAYMOD is shown in Figure 1.The STRATUS Cloud benefits from the layered and mod-ular structure of the XMLAYMOD. This layered andmodular structure enables the STRATUS Cloud to

● Support the manufacturing agents to collaboratewith each other in different manufacturing opera-tions. The layered structure of STRURUS enablesthe joint of manufacturing agents to the platform fordata exchange. STRATUS can share manufacturing


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data among the manufacturing agents while everymanufacturing agent has its own data structures.

● Support the manufacturing agents where they aredistributed over the globe. STRATUS is capable ofexchanging the manufacturing data among the dis-tributed manufacturing agents applying its inheritedXML Service Cloud architecture. The embedded

XML Service Cloud in STRATUS enables the man-ufacturing agents to connect to the STRATUS plat-form for data exchange.

● Support the manufacturing data integration whilethe manufacturing agents collaborate with eachother based on their own data structures. Thisintegration is based on STEP standard data

Figure 1. XMLAYMODoverall platform (Valilai and Houshmand 2013).


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structures. Also applying a modular approachSTRATUS can fulfil the shortcomings of the clas-sical STEP standard limitations. The new modularapproach of the STEP reduces shortcomings suchas reduction of high cost and lengthy time of APs’implementation by reusing the product data in aset of Aps and eliminating duplication andrepeated documentation of the same data entriesin different APs.

Adapting the layered and modular structure ofXMLAYMOD, STRATUS is able to fulfil the threerequirements for a global manufacturing solution dis-cussed earlier. To enable the adoption of optimal solu-tions in global manufacturing enterprises, STRATUSCloud proposes a new contribution based on a service-oriented cloud. This cloud is embedded through theSTRATUS architecture. This cloud is called theOptimization Service Cloud and enables the STRATUSCloud to retrieve the manufacturing data and processthem by its service-oriented optimisation services.Moreover, the results of the executed optimisation ser-vices are stored in STRTUS Cloud. The optimisationservices and its results can be used by the collaborativeagents distributed over the globe during their manufac-turing operations.

Optimization Service Cloud framework

STRATUS Cloud proposes a service-oriented cloud toenable the adoption of optimal solutions in global man-ufacturing enterprises. This Optimization Service Cloudis shown in Figure 2. In the proposed OptimizationService Cloud, the STRTUS Cloud supports the defini-tion of optimisation algorithms in the forms of services.During the service optimisation definition, OptimizationService Cloud reconciles the data structure requirementswith the STEP Management Section. STRATUS Clouddefines the data input requirements of an optimisationalgorithm with the Module Interpretation Bin. Thisoperation is necessary since the optimisation algorithmsneed the manufacturing data as their inputs. The ModuleInterpretation Bin will prepare the optimisation algo-rithms’ data input based on their needed data formatusing the integrated manufacturing data structure ofSTRATUS platform. Moreover, as STRATUS Cloudmaintains the product data integration based on theSTEP standard through different manufacturing opera-tions, it should also maintain the result of optimisationalgorithms’ executions to integrate based on the STEPstandard integrated resources (IRs). The OptimizationService Cloud also supports the reconciling of the opti-misation services’ execution results with the ModuleInterpretation Engine of the STEP ManagementSection. The optimisation services’ execution result

data are stored based on the integrated structures of theSTEP standard. STRATUS Cloud also supports the man-ufacturing agents for retrieving the result of optimisationservices’ execution based on their own data structures.

Another aspect of the Optimization Service Cloudframework is related to its structure and procedures forcommunication with the other sections in STRATUS

Figure 2. Optimization Service Cloud framework.


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Cloud platform. STRATUS Cloud has a layer called theInterface Layer that enables the different manufacturingagents to join the platform for collaboration. The InterfaceLayer is designed to support the different manufacturingagents based on their own data structures. TheOptimization Service Cloud proposes a structure thatenables the Optimization Cloud to behave like a manufac-turing agent. This enables the Optimization Cloud to jointhe Interface Layer like a manufacturing agent andexchange the optimisation manufacturing data with othersections of the STRATUS platform. Moreover, thisenables the Optimization Service Cloud to be managedin a distributed geographical location in relation with theother sections of the STRATUS Cloud platform.

STRATUS Cloud architecture

STRATUS Cloud has a layered and modular architecture,surrounded by two clouds. The overall structure of theSTRATUS Cloud platform is shown in Figure 3. Adaptingthe structure and procedures in XMLAYMOD platform(Valilai and Houshmand 2013), STRATUS Cloud consistsof two sections. The first section is called the InterfaceSection. This section is responsible for the definition ofdifferent data formats with the related data container usedfor data transmission between the manufacturing agentsand the Interface Layer. The second section is called theSTEP Management Section, which manages the STEPstandard modular behaviour for interpretation of differentdata structures. This section also is designed to facilitate

Figure 3. STRATUS overall platform.


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the transmission of XML-based data structures for infer-ential and interpretation operations.

The STRATUS Cloud benefits from the layers pro-posed in the XMLAYMOD platform. There are four layersin the STRATUS Cloud.

CAx interface layer

This layer supports the operations of product data send/retrieve. This operation is conducted while different man-ufacturing agents uses their own data structures for pro-duct data exchange. This layer consists of data formatchannels each for a definite data format that facilitate theproduct data send and retrieve. The data format channelsare managed and controlled by the Data Format Definitionbin in the Interface Section. The Data Format Definitionbin is reconciled by the Data Container Definition bin thatenables the transmission of manufacturing data to thelower layer.

STEP XML layer

STRATUS Cloud supports the distributed manufacturingagents’ collaboration using the XML-based data contain-ers. To accomplish the reliable manufacturing data trans-mission, STRATUS Cloud uses a layer called STEP XMLlayer. This layer receives the XML batch from the XMLService Cloud. The structure and procedures in this layerare designed based on the recent advantages of STEPstandard in XML structures. Using the inferential rulesand the procedures of the STEP Modules XMLize rulesbased on part 28 in the STEP Management Section, theSTEP XML layer inferences the product data based onXML structure and then maps them to STEP modularisedproduct data structures. Vice versa, the operation is man-aged to map the product data from STEP XML-basedformat to a defined XML batch container.

Modular interpretation layer

This layer is designed to lead the integration of differentproduct data structures. Different product data batches aredelivered to this layer as the result of collaboration amongdifferent manufacturing agents. The structure of this layeruses modular interpretation rules and maps product datafrom different format to data structure of STEP modules.This layer exchanges the integrated product data withstore/retrieve layer to store product data or retrieve theproduct data from this layer (Valilai and Houshmand2013).

Store/retrieve layer

The responsibility of this layer is to send the product datato platform database and retrieve it vice versa. The

procedures of this layer receive the product data accordingto the STEP standard modular data structures. In storingoperations, the procedures in this layer send the productdata to the database for storage operation. The productdata is mapped to IRs of STEP standard. Vice versa, theseprocedures retrieve product data from the database andthen deliver them to the Modular Interpretation Layer inthe format of STEP standard data modules (Valilai andHoushmand 2013). These STEP-based modular productdata will be transmitted to the upper layers for modifica-tion by manufacturing software packages.

XML Service Cloud

To enable the support of distributed manufacturing agents,the STRATUS Cloud also inherits a XML Service Cloudfrom the XMLAYMOD. The XML Service Cloud, whichis service-oriented approach, enables different manufactur-ing agents to collaborate with each other when they aredistributed over the globe. This cloud is designed based onthe XML structure due to its reliable and easy capability(Sormaz et al. 2010; Schuster et al. 2011; Bohm andKanne 2011; Jea, Chang, and Cheng 2011) for manufac-turing data transformation. The service cloud supports themanufacturing agents that are connected to the InterfaceLayer. The XML Service Cloud comprises sections asfollows:

● Infrastructures in the cloud that handle the proces-sing, storage and network computing operations inthe cloud. These infrastructures support the manu-facturing agents to send and receive their productdata from XML cloud. Different data format chan-nels in the Interface Layer are connected to XMLService Cloud by means of these infrastructures thatare distributed over the globe (Valilai andHoushmand 2013).

● Service Bin, which organises the XMLizer/DeXMLizer and XML queuing services. These ser-vices are responsible to put the manufacturing datato XML data structures or vice versa extract themanufacturing data from XML data structures fordelivery to related data format channels. XMLqueuing service is used to provide the queuingoperation for service execution. The other servicesdefined in the Service Bin are XML Batch transmit-ter for transmission of XML batch data throughdifferent sections and XML security services forthe verification of manufacturing agents to datastructures.

● XMLization/DeXMLization rules, which organisethe rules for definition of XMLization/DeXMLization services for different data formatdefinitions in the XML Service Cloud.


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● XML Queue section, which is the queue for theXML batches. The XML Queue section organisesthe XML batches by using the XML queuing ser-vice. The order of XMLization/DeXMLization ser-vice execution is managed by this section.

● XML Service Hub, which provides the product datain the form of XML batches with their requiredservices for different operations. The XML ServiceHub is the joint of XML Service Cloud with the outcloud manufacturing universe. XML Service Huborganises the operations of different sections of theXML Service Cloud with each other.

The proposed Optimization Service Cloud

As mentioned earlier, STRATUS Cloud proposes a ser-vice-oriented cloud to fulfil the requirement for enablingthe adoption of optimal solution algorithms. This cloudcomprises sections that enable it to receive the manufac-turing data from other sections of the STRATUS Cloud,execute the optimisation services and deliver the result tothe platform. Besides, the proposed structure and proce-dures in the Optimization Service Cloud support the defi-nition and organisation of optimal services in the platform.The proposed Optimization Service Cloud is based on thecloud manufacturing paradigm. The optimisation algo-rithms are treated as services. The manufacturing datascope in which the optimisation algorithms propose theiroptimum solutions are defined using the IRs of the STEPstandard. The STRATUS platform reconciles data toenable the interpretation of the input and output datarelated to the optimisation services. The OptimizationService Cloud comprise the following sections.

XML Optimization Hub. This section acts as a manufactur-ing agent that connects to the Interface Layer. As theproposed contribution in the paper is based on manufac-turing paradigm, the optimization cloud is designed tocommunicate with the STRATUS Cloud platform like anagent located in the globe. Moreover, as different optimi-sation algorithms need different inputs, in different datastructures, it is required that the XML Optimization Hubsupport the required data format channels each for adefinite optimisation algorithm. The XML OptimizationHub is responsible for receiving the input data for optimi-sation services in the form of an XML Batch. Vice versa,returning the result of the executed optimisation services,the Optimization Hub exchanges the result data with theInterface Layer.

The XML Optimization Hub is designed to work withXML data forms. This feature enables the different sec-tions in the optimisation cloud to be distributed in differ-ent geographical locations. In the cloud, the XMLOptimization Hub is in interaction with the Optimization

Service Queue to deliver the input manufacturing data andalso with the Optimization Service Execution Frameworkto retrieve the resulting data from the optimisation serviceexecution. The XML Optimization Hub also provides theXMLization/DeXMLization Services for the OptimizationService Execution Framework to enable it for extractingthe input parameter for the optimisation services and viceversa delivering the output data in XML forms.

Optimization Service Queue. This section acts in the sameroutine as the XML Queue section acts in the XMLService Cloud. The Optimization Service Queue operatesas a queue that gets the manufacturing data input in theXML forms. This section then organises the XML batchesfor execution. The Optimization Service Queue deliversthe XML batches to the Optimization Service ExecutionFramework.

Optimization Service Execution Framework. This sectionis responsible for the execution of optimisation services inthe Optimization Service Cloud. Where the XML batchdata are delivered to this section from the OptimizationService Queue, this section calls the required optimisationservice from the Optimization Service Management.Optimization Service Execution Framework executes theoptimisation services and delivers the execution results tothe XML Optimization Hub.

Optimization Service Management. This section includestwo bins; the first bin is called the Optimization ServiceBin. The optimisation algorithms are stored in the form ofservices in this bin. When the Optimization Service Cloudneeds to execute a definite rule, the Optimization ServiceManagement retrieves the needed optimisation service anddelivers it to the Optimization Service ExecutionFramework. This bin receives the new optimisation ser-vices from Optimization Service Definition bin.

The second bin is called the Optimization ServiceDefinition bin. Where a new optimisation algorithm isneeded to be defined in the Optimization Service Cloud,this section defines the input data structure and require-ment for the mentioned services. This bin interacts withthe Module Interpretation Engine in the STEPManagement Section. The required data structure interpre-tations are defined in the Module Interpretation Enginebased on the STEP standard application modules. Thedefined procedures are reconciled through STEP ModuleXMLize Rules and XML Inferential Section to enable theSTRATUS Cloud manage the optimisation input and out-put data. STRATUS Cloud can retrieve the input data foran optimisation service from the STEP-based data struc-tures and deliver it to the XML Optimization Hub with therequired data format of optimisation service. Vice versa,when the optimisation service execution is finished, theresulted data are sent back through the Interface Layer to


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the STRATUS Cloud layers. The Modular InterpretationLayer then processes the resulted data and stores the resultdata in product data based on the STEP standard datastructures.

Case study scenarios

In this section, the authors provide two different scenariosto demonstrate the capabilities of the STRATUS Cloud infulfilling the essential requirements of a global manufac-turing enterprise. All the manufacturing agents in thebelow scenarios are assumed to be distributed over theglobe. STRATUS Cloud should support the distributedmanufacturing agents’ collaboration and integration ofthe manufacturing data based on the STEP standard.Moreover, the capability of the proposed OptimizationService Cloud in the STRATUS Cloud to support theoptimisation agents will be discussed comprehensively.

Scenario 1:optimisation of geometry described by curves

In this scenario, a CAD agent is considered to design thegeometry of a blade for vertical axis turbines. It is plannedthat an optimisation algorithm proposed by Westberg(1987) processes the CAD information of the blade andproposes an optimal design for the blade geometry. TheCAD agents choose the .WRL data format (virtual worldcreated in VRML) for design and prepare the design ofvertical axis turbine. The product design data are shown inTable 2. There is the .WRL data format definition in theInterface Section of the platform. Using the related .WRLdata container definition for .WRL data format definition,the Interface Section connects to XML Service Cloud. Itrequests for .WRL XMLizer Service Cloud. The XMLService Cloud uses XML security service and permitsthe Interface Section to use the .WRL XMLizer service.The Interface Section of the platform then requests forXML transmitter service. The XML Service Hub usesthe XML transmitter service and delivers the .WRLXML batch to XML transmission channel. The XMLService Cloud then uses the XML Queuing service todeliver the .WRL XML batch to STEP XML Layer.

The STEP XML Layer uses XML Inferential Section.It uses modular IRs for .WRL data format. It maps theproduct data to STEP standard modular data format asshown in Table 2. The modular STEP-based productdata are formed based on part 28 XML late binding for-mat. The STEP XMLized batch is delivered to theModular Interpretation Layer. The Modular InterpretationLayer uses module interpretation rules and maps the STEPXMLized batch to required IRs of the STEP standard asshown in Table 2.

The version of modular STEP used in STRATUSCloud is based on released ISO/TS 10303-203:2005 edi-tion, which considers AP203 based on modular view.

Now, the product CAD data are based on the STEPstandard and then it is delivered to the store/receivelayer. This layer sends the CAD product data to platformdatabase. The product data are stored based on STEPstandard data format.

After the blade design is completed, the case studyscenario considers that the optimisation algorithm pro-posed by Westberg (1987) starts processing the CADgeometry information of the blade to propose an optimaldesign. This algorithm accepts free format design informa-tion of the blade in the form of a spline curve and per-forms the optimisation processes. This service isembedded in the Optimization Service Cloud. Therequired interpretation rules for this optimisation serviceare defined in the Module Interpretation Bin. The XMLOptimization Hub requests for the blade geometry datathrough the simple txt data format channel. Retrieving theblade design data from database in the form of STEPstandard IRs, the Module Interpretation Engine uses theappropriate rules and interprets the blade geometry data inthe form of the ISO 10303: part 28 and delivers the bladecurve geometry data to the STEP XML Layer. The STEPXML Layer uses the STEP Module XMLize Rules andIRs and maps the blade geometry data to free txt format inthe XML form as shown in Table 3. The blade geometrydata are then delivered to the XML Service Cloud to besent to the Interface Layer. The Interface Layer thendelivers the blade geometry data to the XMLOptimization Hub. Transmitting through the XMLService Queue, the blade design data are delivered to theOptimization Service Execution Framework, where theWestberg Optimization Service is called from theOptimization Service Management Bin. TheOptimization Service Execution Framework exports theinput parameters for the Westberg Optimization Serviceby using the DeXMLization Rule for free txt format asshown in Table 3. The Westberg Optimization Service isthen executed using the input data that declare the bladecurve as shown in Figure 4.

After the optimisation service is executed, the revisedblade curve geometric data are formed in the XML struc-ture based on text format XMLization rules. Then theOptimization Service Execution Framework delivers opti-mised blade design data to the XML Optimization Hub.The XML Optimization Hub then sends the optimised datathrough the Interface Layer to the XML Service Cloud tobe sent to the STRATUS Cloud database. After the XMLService Cloud delivers the optimised data to the STEPXML Layer, this layer uses the proper IRs and map theXML optimised data to the STEP standard data structurein the form of ISO 10303: part 28 and then delivers it tothe Modular Interpretation Layer. The ModularInterpretation Layer uses the Module Interpretation ruleand interprets the optimised data to the original bladedesign based on the STEP standard IRs.


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Table 2. Interpretation of the case study blade for CAD agent through SRATUS Cloud layers.

Blade design in .WRL data format in CAx interface layer

Interpreted blade design based on STEP data format

#VRML V2.0 utf8 WorldInfo { info [ "File created using STRATUS Cloud version 11.0.0.3" ] } NavigationInfo { type [ "EXAMINE" , "WALK" , "FLY" ] } Background { skyColor [ 0 0 0 ] } Viewpoint { position 5.806350 -2.824526 0.522834 orientation -0.134664 0.795576 -0.590699 1.967532 fieldOfView 0.471225 description "Main Viewpoint" } . . geometry IndexedFaceSet { solid FALSE coord Coordinate { point [ 18.05 1.65206e-014 29.16, . . 20 0 -29.16, ] } normal Normal { vector [ 0 0 1, . . 0 0 -1, 0 0 -1, ] } . . Transform { translation 288.701 10 2.82066e-012 rotation 6.93889e-018 -6.93889e-018 1 -1.5708 children [ Group { children [ Group { children [ USE _089D3CA8 ] . . ] }

<?xml version="1.0" encoding="utf-8"?> <iso_10303_28 representation_category="LB"> <express_data id="STRATUS_Cloud"> . . <data_section_header> <documentation>SUT_ IE_STRATUS_Cloud_case_STUDY_ VERSION_10.0.00.</documentation> </data_section_header> <schema_instance id="AP203" express_schema_name="CONFIG_CONTROL_DESIGN"> <entity_instance id="#5" express_entity_name="product"> <attribute_instance express_attribute_name="identifier"> <string_literal>CaseStudy_STRATUS_Cloud</string_literal></attribute_instance> <attribute_instance express_attribute_name="frame_of_reference"> <entity_instance_ref refid="#4"/></attribute_instance></entity_instance> . . <entity_instance id="#65" express_entity_name="CARTESIAN_POINT"> <attribute_instance express_attribute_name="name"><string_literal/></attribute_instance> <attribute_instance express_attribute_name="identifier"><string_literal/></attribute_instance> <attribute_instance express_attribute_name="Orientation"><list_literal> <real_literal>5.806350</real_literal> <real_literal>-2.824526</real_literal> <real_literal>0.522834</real_literal></list_literal></attribute_instance> </entity_instance> . . <entity_instance id="#4634" complex_entity_data_type=""> <entity_instance SUPERTYPE="" express_entity_name="BOUNDED_CURVE"> <entity_instance express_entity_name="B_SPLINE_CURVE"> <attribute_instance express_attribute_name="degree"> <Integer_literal>3</Integer_literal> </attribute_instance> <attribute_instance express_attribute_name="control_points_list"> <list_literal> <attribute_instance express_attribute_name="cartesian_point"><entity_instance_ref refid="#1862"/></attribute_instance> . . <attribute_instance express_attribute_name="cartesian_point"><entity_instance_ref refid="#1854"/></attribute_instance> </list_literal> </attribute_instance><attribute_instance express_attribute_name="Curve_form"><string_literal>.UNSPECIFIED.</string_literal></attribute_instance> <attribute_instance express_attribute_name="closed_curve"><Logical_literal>.F.</Logical_literal></attribute_instance> <attribute_instance express_attribute_name="self_intersect"><Logical_literal>.F.</Logical_literal></attribute_instance> </entity_instance> <entity_instance express_entity_name="B_SPLINE_CURVE_WITH_KNOTS"> <attribute_instance express_attribute_name="knot_multiplicities"> <list_literal> <Integer_literal>4</Integer_literal> <Integer_literal>4</Integer_literal> </list_literal> </attribute_instance> <attribute_instance express_attribute_name="knots"> <list_literal> <real_literal>5.898788532683943800</real_literal> <real_literal>6.283185307179586200</real_literal> </list_literal> </attribute_instance> <attribute_instance express_attribute_name="knot_spec"><string_literal>.UNSPECIFIED.</string_literal></attribute_instance> </entity_instance></entity_instance> . . </entity_instance> . . <entity_instance id="#4635" express_entity_name="MANIFOLD_SOLID_BREP"> <attribute_instance express_attribute_name="name"> <string_literal>BearingFace</string_literal> </attribute_instance> <attribute_instance express_attribute_name="outer"><entity_instance_ref refid="#9639"/> </attribute_instance></entity_instance> . . </schema_instance></express_data></iso_10303_28>

(continued )


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Scenario 2: optimisation of cutting speed in a CNCmachining operation

In this scenario, a CNC process planning agent known asAgent I is considered to prepare the CNC machiningcode and proposes process planning data of a part

shown in Figure 5. The STRATUS Cloud should storethe CNC process planning data based on the STEP stan-dard IRs. In what follows, Agent I requests that anoptimisation algorithm processes the CNC process planand proposes the optimum cutting speed to minimise the

Table 2. (Continued).

Product design data interpretation to product data Modules of STEP Standard

Product design modules mapped to Integrated Resources of STEP Standard

Product design CA

D data

emadnuF :14 traP lavorppa :2101 eludoM PA ntals of product Description and Support Part 42: Geometric and Topological Representation. . . . . . . Part 101: Draughting

AP Module 1010: date_time AP Module 1011: person_organization . . . AP Module 1015: security_classification . . . AP Module 403: AP203 Configuration control 3d design

Table 3. Interpretation of the geometry case study blade for the Westberg Optimization Service through the SRATUS Cloud layers.

Blade design data in a XML batch for free txt data format DeXMLized Blade design data for Westberg Optimization Service

<?xml version="1.0"?>

<Data_Channel_Batch><GUID>009736820LOPTTRGBHO0322</GUID> <Header_Freetxt> FreeTxt utf8 </Header_Freetxt>

<HeaderInfo> "File created using STRATUS Cloud version 11.0.0.3" </HeaderInfo> <SPLine><Degree> 3 </Degree> <Points><Point><X> 28.467 </X><Y>-0.375 </Y><Z> 2.852 </Z></Point> . . <point><X> 18.419 </X><Y>0.0000 </Y><Z> 3.345 </Z></point></Points>

</SPLine></Data_Channel_Batch>

GUID { 009736820LOPTTRGBHO0322 } spline { parameters { Degree=3; Points= (28.467,-0.375,2.852 24.35,-2.55,2.900 22.19,-1.28,2.925 18.419,0.0000,3.345); }}

Optimised blade design data after Westberg Optimization Service execution

XMLized Blade design data after Westberg Optimization Service execution

GUID { 009735545LIHNEEGPLO7667 } spline { parameters { Degree=3; Points= (28.470,-0.380,2.85 24.46,-2.47,2.950 22.00,-1.26,2.900 19.960,1.230,3.445); }}


<Data_Channel_Batch><GUID>009735545LIHNEEGPLO7667</GUID> <Header_Freetxt> FreeTxt utf8 </Header_Freetxt>

<HeaderInfo> "File created using STRATUS Cloud version 11.0.0.3" </HeaderInfo> <SPLine><Degree> 3 </Degree> <Points><Point><X> 26.468 </X><Y>-0.876 </Y><Z> 2.673 </Z></Point> . . <point><X> 19.963 </X><Y>0 .004</Y><Z> 3.467 </Z></point></Points>

</SPLine></Data_Channel_Batch>


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part production time based on the famous Taylor tool lifeequation and machining economics credited by Gilbert(1950) (Wee, Venkatesh, and Goh 1988; Calur andGurarda 1998). The STRATUS Cloud should retrieve

the CNC process plan data of Agent I and send theCNC process plan to Optimization Cloud for optimisationservice. Finally, when the optimisation service processedthe CNC process plan data, the resulted optimised cutting

Figure 4. Blade curve input and optimised curve points by Westberg Optimization service.

Figure 5. CNC machining code generation.


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speed data should be sent back to the platform andshould be stored in the platform updating the Agent ICNC process planning data.

Agent I as a CNC process planning agent generates theCNC machining G-codes for SINUMERIK 810/820 Mpost processors. After the Agent I generates the G-codesand modifies the tool and process planning operations, ituses the related .dfm/.M01 data container definition for .dfm and .M01 data format definition in the InterfaceSection. The Interface Section connects to XML ServiceCloud. It requests for .dfm/.M01 XMLizer Services. TheXML Service Cloud uses XML security service and per-mits the Interface Section to use the .dfm/.M01 XMLizerservice. The CNC machining data of the part is formed ina XML batch based on .dfm/.M01 data format. TheInterface Section then requests for XML transmitter ser-vice. The XML Service Hub uses the XML transmitterservice and delivers the .dfm/.M01 XML batch to XMLtransmission channel. The XML Service Cloud then usesthe XML queuing service to deliver the XML batches toSTEP XML Layer.

The STEP XML Layer uses modular Interpretationrules for .dfm/.M01 data format for the XML batch byrequesting the required procedures from the XMLInferential Section. It maps the machining data of thepart to the STEP standard modular data format. Theauthors adopted the necessary AAM, MIMs from ISO/TS 10303-203:2005 edition and also ISO 10303-238:2007. It should be mentioned that STEP applicationprotocols and modules are under continuous improve-ments. The authors have developed the necessary inter-pretation structures for modules that are not developed yetto map the STEP-NC data to STEP integrated resources.These procedures are working temporary and will bereplaced when a new improvement in application modulesis achieved or any new application module is introduced,but the product integrity based on STEP integratedresources is always maintained. The modular STEP-based product data are formed based on part 28 XMLlate binding format as shown in Table 4. The STEPXMLized batches are then delivered to the ModularInterpretation Layer. The Modular Interpretation Layeruses module IRs and maps the STEP XMLized batch tothe required IRs of the STEP standard as shown inTable 4. The product CAD/CAM/CNC machining dataare now based on the STEP standard and then deliveredto the store/receive layer. From this layer, the CNCmachining data are sent to the platform’s database andstored based on STEP standard IRs.

After the CNC machining code for the part is gener-ated, the scenario assumes that the Gilbert OptimizationService processes the CNC machining data for optimisedcutting tool speed. This algorithm works with machiningdata in text format related to the tool Taylor specificationsand the tool change time. The Gilbert Optimization

Service is embedded in the Optimization Service Cloud.The required interpretation rules for this optimisation ser-vice are defined in the Module Interpretation Bin. TheXML Optimization Hub requests for the required data ofthe tool Taylor specification and tool change time definedin the database through the simple txt data format channel.Retrieving the part CNC machining data and the toollibrary from database in the form of STEP standard IRs,the Module Interpretation Engine uses the appropriaterules and interprets the tool data in the form of the ISO10303: part 28 and delivers the needed tool data to theSTEP XML Layer as shown in Table 5. The STEP XMLLayer uses the STEP Module XMLize Rules XML andInferential Rules and maps the tool data to free txt formatin the XML form as shown in Table 5. The tool specifica-tion data and the related tool change time for CNCmachining operation are then delivered to the XMLService Cloud to be sent to the Interface Layer. TheInterface Layer then delivers the tool data to the XMLOptimization Hub. Transmitting through the XML ServiceQueue, the tool data besides the tool change time data aredelivered to the Optimization Service ExecutionFramework, where the Gilbert Optimization Service iscalled from the Optimization Service Management Bin.The Optimization Service Execution Framework thenexports the input parameters for the Gilbert OptimizationService by using the DeXMLization Rule for free txtformat as shown in Table 5. The Gilbert OptimizationService is then executed on the input data declaring theoptimum cutting speed for the CNC machining operation.

After the optimisation service is executed, the opti-mised cutting speed data are formed in the XML structureusing the free txt format XMLization rule and then theOptimization Service Execution Framework delivers opti-mised cutting speed data to the XML Optimization Hub.The XML Optimization Hub then sends the optimised datathrough the Interface Layer to the XML Service Cloud tobe sent to the STRATUS Cloud database. After the XMLService Cloud delivers the optimised data to the STEPXML Layer, this layer uses the proper Inferential Rulesand map the XML optimised data to the STEP standarddata structure in the form of ISO 10303: part 28 and thendelivers it to the Modular Interpretation Layer. TheModular Interpretation Layer uses the ModuleInterpretation rule and interprets the optimised data tothe original CNC machining operation based on theSTEP standard IRs.

Conclusion

The synergy of fundamental factors such as changes ingovernmental policies, global expansions of the manufac-turing industries and improvements in technology relatedto reliability of manufacturing information flow have cre-ated the global manufacturing revolution in the first years


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Table 4. Interpretation of the case study product for Agent I CNC process planning agent through STATUS Cloud layers.

Part CNC machining data in .M01/.dfm data format in CAx interface layer

Interpreted CNC machining data of the part based on STEP data format

CadFiles=0 NcFiles=1 [WorpieceOffsets] ClampVector=-10.000000,-50.000000,35.000000 Fixture=100.000000,50.000000,40.000000 ClampMoveX=0 [RawPart] Extensions=100.000000,50.000000,40.000000 [Tool1M] IndexPool=41 CutRadius=0.000000 ToolKind=33 ToolOffsetX=0.000000 ToolOffsetZ=0.000000 ToolOffsetY=0.000000 ToolName=Disk milling c. 50mm Comment=HSS, Bore 16mm ToolLength=10.000000 ToolAngel=0.000000 CutKind=0 . . [Tool10M] . . (* WinCAM by EMCO [c] 95-96, NC-program ISO format M *)

N0005 G54 N0010 G97 G94 N0015 G58 X0. Y0. Z0. N0020 T4 D7 M0 N0025 M5 N0030 F100 N0035 S200 N0040 M3 N0045 T4 D7 M0 N0050 M3 N0055 G0 Z10. . . N0310 G1 Z0 N0135 M30

<?xml version="1.0" encoding="UTF-8"?> <iso_10303_28 representation_category="LB"><express_data id="STRATUS Cloud">. . <data_section_header><documentation>SUT_ IE_STRATUS_Cloud_case_STUDY _VERSION_11.0.00_CAM_CNC_Part</documentation> </data_section_header> <schema_instance id="AP238" express_schema_name="STEP-NC Integrated CNC"><entity_instance id="#1" express_entity_name="PROJECT"><attribute_instance express_attribute_name="identifier"><string_literal>CNC code generated for STRATUS Cloud</string_literal> </attribute_instance> <attribute_instance express_attribute_name="MACHINING_WORKINGSTEP"><entity_instance_refrefid="#2"/> </attribute_instance> <attribute_instance express_attribute_name="WORKPIECE"><list_literal><entity_instance_ref refid="#3"/></list_literal> </attribute_instance> </entity_instance> . . <entity_instance id="#17" express_entity_name="DRILLING"><attribute_instance express_attribute_name="identifier"><string_literal> Disk milling c. 50mm </string_literal> </attribute_instance> <attribute_instance express_attribute_name="CUTTING_TOOL"><entity_instance_ref refid="#20"/> </attribute_instance> <attribute_instance express_attribute_name="MILLING_TECHNOLOGY"><list_literal><entity_instance_ref refid="#25"/></list_literal> </attribute_instance><attribute_instanceexpress_attribute_name="MILLING_MACHINE_FUNCTIONS"><list_literal><entity_instance_ref refid="#28"/></list_literal> </attribute_instance></entity_instance> . . <entity_instance id="#45" express_entity_name="CARTESIAN_POINT"><attribute_instance express_attribute_name="name"><string_literal/></attribute_instance><attribute_instance express_attribute_name="identifier"><string_literal/> </attribute_instance><attribute_instance express_attribute_name="coordinates"><list_literal><real_literal>0</real_literal> <real_literal>0</real_literal> <real_literal>0</real_literal></list_literal> </attribute_instance></entity_instance> . . <entity_instance id="#378" express_entity_name="MANIFOLD_SOLID_BREP"><attribute_instance express_attribute_name="name"><string_literal>PartBody</string_literal></attribute_instance> <attribute_instance express_attribute_name="outer"><entity_instance_ref refid="#57"/></attribute_instance> </entity_instance> . . </schema_instance> </express_data> </iso_10303_28>

Product CNC machining data interpretation to product data modules of STEP standard

Product CNC machining modules mapped to integrated resources of STEP standard

Product CN

C m

achining data

F:14traPlavorppa:2101eludoMPA undamentals of product Description and Support Part 42: Geometric and Topological Representation . . . Part 47: Shape variation tolerances Part 49: Process structure and properties . . . Part 101: Draughting . . . Part 514:Advanced boundary representation

AP Module 1010: date_time AP Module 1011: person_organization . . AP Module 1015: security_classification . .

AP Module 403: AP203 Configuration control 3d design

AP Module 1712: Part feature function AP Module 1714: Part feature location . .

AP Module 1101: Product property feature definition;


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Table 5. Interpretation of the CNC machining part for the Gilbert Optimization Service through the SRATUS Cloud layers.

Tool specification and tool change data retrieved from database in the form of the ISO 10303: part 28 <?xml version="1.0" encoding="UTF-8"?> <iso_10303_28 representation_category="LB"><express_data id="STRATUS Cloud">. . <data_section_header><documentation>SUT_ IE_STRATUS_Cloud_case_STUDY _VERSION_11.0.00_CAM_CNC_Part</documentation> </data_section_header> <schema_instance id="AP238" express_schema_name="STEP-NC Integrated CNC"><entity_instance id="#1" express_entity_name="PROJECT"><attribute_instance express_attribute_name="identifier"><string_literal>CNC code generated for STRATUS Cloud</string_literal> </attribute_instance> <attribute_instance express_attribute_name="WORKPIECE"><list_literal><entity_instance_ref refid="#3"/></list_literal> </attribute_instance> </entity_instance> . . <entity_instance id="#17" express_entity_name="DRILLING"><attribute_instance express_attribute_name="identifier"><string_literal> Disk milling c. 50mm </string_literal> </attribute_instance> <attribute_instance express_attribute_name="CUTTING_TOOL"><entity_instance_ref refid="#20"/> </attribute_instance> . . <entity_instance id="#638" express_entity_name="MACHINING_SPINDLE_SPEED_REPRESENTATION"><attribute_instance express_attribute_name="name"><string_literal>spindle speed</string_literal></attribute_instance> <attribute_instance express_attribute_name="items"><list_literal><entity_instance_ref refid="#623"/></list_literal></attribute_instance> <attribute_instance express_attribute_name="context_of_items"><entity_instance_ref refid="#637"/></attribute_instance> </entity_instance> <entity_instance id="#623" express_entity_name="MEASURE_REPRESENTATION_ITEM"><attribute_instance express_attribute_name="name"><string_literal>rotational speed</string_literal> </attribute_instance> <attribute_instance express_attribute_name="numeric_measure"><real_literal>200.</real_literal></attribute_instance> <attribute_instance express_attribute_name="Derived_Unit"><entity_instance_refid="#635"/></attribute_instance> </entity_instance>. . <entity_instance id="#1021" express_entity_name="Known_Source"><attribute_instance express_attribute_name="source_item"><string_literal>IDENTIFIER('ISO 13399')</string_literal> </attribute_instance> <attribute_instance express_attribute_name="description"><string_literal>ISO 13584 library</string_literal> </attribute_instance></entity_instance> <entity_instance id="#1013" express_entity_name="externally_defined_class"><attribute_instance express_attribute_name="source_item"><string_literal>IDENTIFIER('76Y88U87K99O0')</string_literal></attribute_instance> <attribute_instance express_attribute_name="description"><string_literal/></attribute_instance> <attribute_instance express_attribute_name="name"><string_literal>end mill</string_literal></attribute_instance> <attribute_instance express_attribute_name="external_source"><entity_instance_ref refid="#1021"/></attribute_instance></entity_instance> <entity_instance id="#1011" express_entity_name="applied_classification_assignment"><attribute_instance express_attribute_name="assigned_class"><entity_instance_ref refid="#1013"/></attribute_instance> <attribute_instance express_attribute_name="role"><entity_instance_ref refid="#1018"/></attribute_instance> <attribute_instance express_attribute_name="items"><list_literal><entity_instance_ref refid="#1003"/></list_literal></attribute_instance></entity_instance> . . <entity_instance id="#2023" express_entity_name="MEASURE_REPRESENTATION_ITEM"><attribute_instance express_attribute_name="name"><string_literal>Tool Change Time</string_literal></attribute_instance> <attribute_instance express_attribute_name="numeric_measure"><real_literal>27.</real_literal></attribute_instance> <attribute_instance express_attribute_name="Derived_Unit"><entity_instance_ref refid="#2087"/> </attribute_instance></entity_instance>. . <entity_instance id="#2023" express_entity_name="MEASURE_REPRESENTATION_ITEM"><attribute_instance express_attribute_name="name"><string_literal>Parameter C in Taylor Equation</string_literal></attribute_instance> <attribute_instance express_attribute_name="numeric_measure"><real_literal>300</real_literal></attribute_instance> <attribute_instance express_attribute_name="Derived_Unit"><entity_instance_ref refid="#2167"/> </attribute_instance></entity_instance> <entity_instance id="#2023" express_entity_name="MEASURE_REPRESENTATION_ITEM"><attribute_instance express_attribute_name="name"><string_literal>Parameter n in Taylor Equation</string_literal></attribute_instance> <attribute_instance express_attribute_name="numeric_measure"><real_literal>0.25</real_literal></attribute_instance>

(continued )


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of the twenty-first century. The concept of global manu-facturing enterprise has created benefits and opportunitiesfor today’s manufacturing enterprises such as reduction ofmanufacturing costs by utilising low labour-cost countries,reduction of business risks and introduction of new mar-kets as a new source for enterprise growth. It should benoted that the implementation of the global manufacturingparadigm has enforced enterprise to endure costs. Besides,among the recent researches conducted to enable the glo-bal manufacturing enterprises, there is still a need forefficient solutions to fulfil the global product developmentrequirements. In this paper, the essential aspects andrequirements of a global manufacturing approach werestudied. Considering the essential requirements such asintegration of manufacturing operations over the globe,support of manufacturing networks in distributed manu-facturing enterprises, collaboration support in global man-ufacturing networks and enabling the adoption of optimal

solutions in global manufacturing enterprises, the paperreviewed the dominant researches in the global manufac-turing approaches.

● Considering the new paradigm for global manufac-turing called cloud manufacturing that enables amajor transformation in manufacturing industry,this paper has proposed a new manufacturing plat-form called STRATUS Cloud for today’s globalmanufacturing. STRATUS Cloud benefits from thelatest work of authors and fulfils the essentialrequirement of today’s global manufacturing para-digm using a service-oriented structure. Of themost, the STRATUS Cloud enables the adoptionof optimal manufacturing solutions using aservice-oriented cloud. This optimisation supportsthe optimal solutions by providing their requiredinput data and receiving the optimised results

Table 5. (Continued).

<attribute_instance express_attribute_name="Derived_Unit"><entity_instance_ref refid="#2214"/> </attribute_instance></entity_instance>. . </schema_instance> </express_data> </iso_10303_28>

Tool specification and tool change data in a XML batch for free txt data format

DeXMLized tool specification and tool change data for Gilbert Optimization Service


<Data_CHannel_Batch><GUID> 103076334864MKEPALK9991 </GUID> <Header_Freetxt> FreeTxt utf8 </Header_Freetxt> <HeaderInfo> "File created using STRATUS Cloud version 11.0.0.3" </HeaderInfo> <Parameter_list>

GUID { 103076334864MKEPALK9991 } Simple Gilbert Problem { parameters

<Parameter><ID> 1 </ID><Parameter_Name> C in Tayleo Equation </Parameter_Name> <Value> 300 </Value><Dimension> m*min(-1) </Dimension></Parameter> <Parameter><ID> 2 </ID><Parameter_Name> n in Tayleo Equation </Parameter_Name> <Value> 0.25 </Value><Dimension> n-a </Dimension></Parameter> <Parameter><ID> 3 </ID><Parameter_Name> Tool change Time </Parameter_Name> <Value> 27 </Value><Dimension> min </Dimension></Parameter></Parameter_list></Data_CHannel_Batch>

{ C=(300,(m,min); n=(0.25); Tt=(27,(min); } }

Optimised Cutting speed data after Gilbert Optimization Service execution

XMLized Optimised Cutting data after Gilbert Optimization Service execution

GUID { 103076334864MKEPALK9991 } Simple Gilbert Problem { parameters { C=(300,(m,min); n=(0.25); Tt=(27,(min); } Outputs { Optimum Spindle Speed Vop=(100,(m,min)); } }


<Data_CHannel_Batch><GUID> 103076334864MKEPALK9991 </GUID> <Header_Freetxt> FreeTxt utf8 </Header_Freetxt> <HeaderInfo> "File created using STRATUS Cloud version 11.0.0.3" </HeaderInfo> <Parameter_list><Parameter><ID> 1 </ID><Parameter_Name> C in Tayleo Equation </Parameter_Name> <Value> 300 </Value><Dimension> m*min(-1) </Dimension></Parameter> <Parameter><ID> 2 </ID><Parameter_Name> n in Tayleo Equation </Parameter_Name> <Value> 0.25 </Value><Dimension> n-a </Dimension></Parameter> <Parameter><ID> 3 </ID><Parameter_Name> Tool change Time </Parameter_Name> <Value> 27 </Value><Dimension> min </Dimension></Parameter></Parameter_list> <OutPutList><output><ID> 1 </ID><Parameter_Name> Spindle Speed </Parameter_Name> <Dimension> m*min(-1) </Dimension></output></OutPutList> </Data_CHannel_Batch>


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related to the optimal solutions. The other mostimportant feature of the STRATUS Cloud that ful-fils the essential requirement of global manufactur-ing can be summarised as

● STEP-based integration of manufacturing data inareas of manufacturing data. Moreover, STRATUSCloud fulfils the shortcomings and limitations thatarise for adaption of the classical STEP standard.

● Support of manufacturing networks in distributedmanufacturing enterprises by the use of XML datastructures as transmission services in the STRATUSplatform.

● Support of collaboration in global manufacturingnetworks by enabling different manufacturingagents to access and share manufacturing datathrough their manufacturing operations.

The capabilities of the STRATUS Cloud framework are dis-cussed in different case study scenarios. For furtherresearches, the authors recommend embedding various man-ufacturing optimisation algorithms adaption in the STRATUSCloud platform to cover all the required optimisation systemsin manufacturing chain. Also, proposing new data models fornew manufacturing operations in the form of optimisationservices in addition to reconcilement by the STEP standardIRs is of interest. Also in the related concepts, areas such asmanagement of capacity from a strategic point of view, plan-ning the development of new processes, scheduling the pro-cesses develop programme, deploying the proper capacities ofresources in operations, the decision-making process for out-sourcing, management of the value chain and linking themanufacturing resources to achieve the optimal performancecan be considered to be included in the STRATUS Cloudplatform. Finally, as the STRATUS Cloud proposes a colla-borative framework, proposal of ideas for enabling the differ-ent manufacturing enterprises for collaboration in the form ofoffering their optimisation services to the manufacturingenterprises as their added value is strongly recommended.

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a platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing...

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