Jyotirmoyee Bhattacharjya

Sonali Tripathi

Andrew Taylor

Margaret Taylor
email: m.taylor4@bradford.ac.uk

David Walters
email: david.walters@sydney.edu.auw.a.taylor

Additive Manufacturing: Current Status and Future Prospects

Keywords: additive manufacturing, 3D printing, business model, sustainability

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Background

In an environment of increasing competition from developing countries, manufacturers in western countries are increasingly focusing on high-end products or unique production processes or environmentally sustainable product lines [START_REF]Economics Intelligence Unit: Aiming Higher, How Manufacturers are Adding Value to their Business[END_REF]. There is growing interest in applications of innovative manufacturing technologies across a number of sectors. This paper focuses on one type of advanced manufacturing practice -additive manufacturing or 3D printing and presents preliminary findings from a survey conducted in the Australasian region.

Trends in Additive Manufacturing

Three-dimensional (3D) printing is an example of additive manufacturing that is becoming increasingly popular [START_REF] Wohlers | Additive Manufacturing and 3D Printing State of the Industry[END_REF] [START_REF]Additive Manufacturing: Opportunities and Constraints[END_REF]. In 2012, the global market for additive manufacturing products and services grew to over USD 2 billion and the unit sales of industrial systems reached around 8000 units. A recent report from the UK [START_REF]Additive Manufacturing Special Interest Group, Technology Strategy Board: Shaping Our National Competency in Additive Manufacturing[END_REF] suggests that although additive manufacturing provides an attractive option for organizations seeking to continue to be competitive in manufacturing, the technology currently has less than 8% market penetration. While this is due to a combination of lack of greater awareness within the global manufacturing community and shortcomings of existing technology, there is a great scope for future growth in this area.

Three key industries, automotive, aerospace and medical, have adopted and seen developments in additive manufacturing technology for various reasons [START_REF] Campbell | Additive Manufacturing: Rapid Prototyping Coming of Age[END_REF]. The automotive sector has used the technology to get new products to market quickly and in a cost effective manner, thereby achieving savings in the overall development of vehicles. The aerospace sector has adopted the technology for its ability to produce very complex, high performance products especially in the context of carbon composite aircraft designs. The ease of converting 3D medical imaging data has made additive manufacturing attractive to the medical industry. However, speed, accuracy, nonlinearity, material properties and system costs remain as limitations of the technology. There are currently several 3D printing machines available to potential and existing commercial users. These printers can work with various materials to produce parts that can be used in rapid prototyping applications [START_REF] Polzin | Characterization and Evaluation of a PMMA-based 3D Printing Process[END_REF].

An overwhelming attraction of additive manufacturing is its ability to meet customer requirements for product customization and product immediacy [START_REF] Gibson | Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing[END_REF]. However, while the rapidly developing technology is well documented, the business implications for manufacturing organizations adopting/using additive manufacturing appears to have received limited attention in the academic literature. This paper aims to address this gap in the literature in the Australasian context.

Research Method and Data Collection

This paper reports on the Australian data from an exploratory survey of users of additive manufacturing conducted in Europe, North America and Australasia by a collaborating team of researchers in the Australasia and the UK. The Australasian users of additive manufacturing (organizations and key contact points) were identified with the help of the Advanced Manufacturing Cooperative Research Centre in Australia. Of the 42 users invited to participate in the study, 17 responded but 13 (31%) actually completed the online survey. The online survey contains both Likert scale questions as well open-ended questions allowing users to describe their experiences with additive manufacturing and their understanding of the current and future of uses of the technology. Although the response rate in Australasia was considerably lower than that in Europe/North America, the data provided useful insights for future in-depth case study based research. The users represented various sectors including consumer durables and disposables, industrial components and products as well as consultancy services (indirect users); ten of the 13 respondents held senior management roles. Most of the organizations (61.5%) have been involved in additive manufacturing for less than five years. Others (23.1%) had been working in the area for five to 10 years or 10 to 15 years (15.4%). A majority of the manufacturing firms were found to be using non-metals in their production processes.

The proportion of respondents involved in different types of additive manufacturing applications is shown in Table 1 below 3 Findings

Current Status of Additive Manufacturing

A number of potential benefits of additive manufacturing have been noted in the press and the academic literature. In this study, a high proportion of respondents (77%) strongly agreed that the reduced time lag between design and production was a benefit of this type of manufacturing. This is aligned with common assertions in the press in relation to this technology. The top three benefits identified by respondents are shown in Figure 1 below. When describing their experiences with output quality from additive manufacturing, majority of the respondents (69%) were positive while noting issues with materials, machines/platforms and outputs. One respondent made the following observation on platform and material issues while noting the quick turnaround time: "We have just completed an extremely busy two months. There is no way we could have met demand with out our additive manufacturing system. Consistency is dependent on understanding the accurate areas on the printing platform. Printing the same file on different areas of the platform will result in dimension differences. The surface finish is consistently better than hand made prosthetics, in the majority of parts. The part material we use is an acrylic. It is a little brittle." Another respondent noted the process improvements that had occured over time while observing that the technology had a long way to go: "We have been using additive manufacturing for 15 years in a prototyping function. The quality of the processes is improving in our experience and we can prototype parts which appear very close to the production equivalent. However we are yet to see an additive process that can replace traditional production processes in terms of physical material properties -strength, durability, stability. This is especially true for processes which involve work hardening or internal stress loading".

The adoption of additive manufacturing has impacts on a business through the requirement for additional resources and staffing/training as well as value creation through improved turnaround times and positive changes in business-to-business interactions. One respondent from heathcare manufacturing provided the following insights: "Two thirds of our production line required retraining. Interest in our business increased, so the profile of our business attracted more attention from our current customers, new customers and competitors. Subsequently this opened new revenue sources. We are subcontracting to new clients who were previously competitors. We are building a network of Dental laboratories in Australia and New Zealand that we will be training to utilize additive manufacturing in their businesses. Our business has now international markets, where previously our market was restricted to domestic Dental Laboratories." Another senior respondent from the automotive sector noted: "Additional facilities and staff added, increase in products developed with reduced turnaround times. Quality of our tool makers has increased dramatically; now 3D assembled parts can be sent to them before tooling starts as it allows them to visualize parts we are developing as a whole, this helps deliver a better quality part as well as speeds up tooling process. Rapid prototyping is now one of the company's biggest marketing assets with every potential customer being given a site visit and being shown the prototype machines' potential."

Future of Additive Manufacturing

When asked to identify the usefulness of potential developments in additive manufacturing equipment/materials, a high proportion of respondents (75%) strongly agreed that further decreases in printer cost would be useful to them. The top four useful potential developments identified by respondents are shown in Figure 2 below. Additive manufacturing was viewed to be an environmentally sustainable approach by 77% percent of the respondents. Respondents identified reduction in material wastage and energy usage as key reasons why this approach to manufacturing was a greener solution. Reduction in transportation related energy usage by manufacturing at the point of use was also noted.

Respondents were divided as to the potential for additive manufacturing to increase on-shoring/re-shoring of manufacturing actives in the near future. Around 46% of respondents believed that additive manufacturing creates opportunities for local suppliers. While noting volume limitations, one respondent reported positive changes in their own practices: "Unless the quality of finished parts improves for volume manufacturing we will continue to largely use the technology for prototyping....having said that we are now getting prototype parts done locally where they were regularly manufactured offshore so a net benefit." However, the developments in additive manufacturing in China (e.g., [START_REF] Anderson | Additive Manufacturing in China: Threats, Opportunities and Developments (Part 1)[END_REF]) have led to counterarguments from other respondents: "We find that for our purposes this is not a consideration. Rapid prototyping is based on speed of TAT [turnaround time] and quality of the result. We regularly use Chinese manufacturers for this."

Most respondents (77%) agreed that given President Obama's announcement of funding for a National Additive Manufacturing Innovation Institute (America Makes) in 2012, other governments should invest in more additive manufacturing research activity. Reflecting on the Australian context, one respondent made the following observation: "To become competitive and stay competitive in the changing world, we must be leaders in technology. There is still so much to learn and so much to be gained by becoming a global leader in additive manufacturing. It is unfortunate Australia has missed the boat in the early years of Additive Manufacturing technology. But there is still much to be learned, I have many questions that cannot be answered. I have much to learn. How will I continue to develop my underpinning knowledge with no resource tools? Additive Manufacturing is the future. If we want to be a part of the future we must invest in the future." However, others raised issues such as the consequences of too great a concentration of funding on one type of technology at the expense of others and the need for any research/development in the area to be industry led. Irrespective of their views on funding of relevant research activities, all respondents identified a number of future applications of additive manufacturing as listed in Table 2 below. Complex parts at low volumes; production parts that only have material when required, in shapes that can only be produced by this method of manufacturing Consumer durables Custom components specific to a consumer e.g. based on environment, user etc.; possibility of "making your own parts" by using printing machines and files thus reducing inventory and freight costs Specialist consumer products (e.g., apparel) Ability to make unique design apparel without tooling costs; jewellery; many applications some of which are already here: mobile phone cases; shoes and glasses etc. Tools for assembly purposes Ease of manufacture of tooling without setup costs of traditional manufacturing methods Other Consumer customized products; applications in every industry not just manufacturing

Discussions

This paper presents the outcomes of a preliminary analysis of data from an exploratory survey on current practices and potential future development in the additive manufacturing sector from the perspective of users of the technology in the Australasian region. The findings suggest that users recognize the benefits of additive manufacturing as well as the developments required in future in order for the technology to continue adding value to their businesses. Adoption of the technology appears to bring with staffing, training and other resource needs but it has the potential to change the way companies grow and interact with their customers and suppliers. All respondents in this study seemed to recognize that the technology is here to stay despite its current shortcomings and believed there would be a range of potential applications.

A recent article in The Guardian describes the developments in metal 3D printing as furthering a second industrial revolution [START_REF] Gibbs | Metal 3D Printing and Six Key Shifts in the 'Second Industrial Revolution[END_REF]. These developments include the availability and decreasing costs of metal 3D printers for home use, 3D printers that can build themselves, current research on 3D printers that can produce nutritious food for astronauts, affordable 3D scanning and 3D printing as a service. The fact that many 3D printing patents will soon expire is also expected to create opportunities for greater innovation [START_REF] Hornick | Many 3D Printing Patents are Expiring Soon: Here's a Round Up & Overview of Them[END_REF].

The potential of the technology to revolutionize manufacturing has been recognized by governments, the private sector and university researchers. As noted by the respondents in this study, the technology is still being used largely for prototyping purposes. Overcoming the limitations of the technology in order for it to be used for high-volume manufacturing remains a challenge for all interested stakeholders. Addressing the challenge will require collaboration across different sectors. In 2012, America Makes was established in the US as a network of around 100 academic institutions, companies, government agencies and non-profit organizations in order to make the country's additive manufacturing industry globally competitive. [11] In the UK, three companies, Renishaw, Hieta and Sysemia, are collaborating to address the mass-production related limitations [START_REF] Bharadwaj | Mass Production using Additive Manufacturing? Yes, Says a New Research Collaboration[END_REF]. The project is funded by the Technology Strategy Board, the UK government's innovation agency.

As the technology becomes more scalable, small businesses that are adopting the technology in Australia will be able to experience significant market growth. Ondemand manufacturing may also become more common. Companies such as Sculpteo [13] and Shapeways [14], for instance, are already at the forefront of creating online marketplaces around 3D printing. The range of business impacts of the developments in additive manufacturing will become clearer over the next few years. This paper is an initial exploration of these developments in additive manufacturing. The authors expect to conduct further research to understand the impacts of collaborative research in additive manufacturing as well as the implications of these developments for business models.

Fig. 1 .

 1 Fig.1. Benefits of additive manufacturing based on the experiences of respondents (SD= strongly disagree, D=disagree, NA/D=neither agree nor disagree, A=agree, SA=strongly disagree)

Fig. 2 .

 2 Fig. 2. Usefulness of potential developments in additive manufacturing equipment/materials (NAU=not at all useful, OLU=of limited use, NV=neutral view, U=useful, VU=very useful)

Table 1 .

 1 . Respondent involvement in additive manufacturing applications

	Additive manufacturing applications	% of
		Respondents
	Rapid prototyping for new product development	69.2
	Rapid manufacturing (complete products)	7.7
	Rapid manufacturing (product components that subsequently become	38.5
	part of a complete product)
	Other	7.7

Table 2 .

 2 Respondent perspectives on future applications of additive manufacturing

	Industry	Future applications
	Healthcare	Low volume manufacturing/prototyping; full and partial dentures,
		splints, surgical guides, orthodontics; rapid prototyping; custom
		components specific to patent, e.g., based on biometric detail
	Automotive	Low volume spares and tooling; new product testing and trialing;
		spare parts; rapid production of replacement parts for repair shops as
		well as rapid prototype
	Aerospace


M Biancucci
email: michelebiancucci@gmail.com

J Janeiro
email: j.janeiro@tudelft.nl

M Leva

S Lukosch
email: s.g.lukosch@tudelft.nl

M Mecella
email: mecella@uniroma1.it

Collaboration in Virtual Enterprises through the Smart Vortex Suite

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The last years have witnessed the emergence of a trend in the industry that fosters the offer of intangible assets within vendor-customer relations even if tangible assets still represent a high volume and a very precious part of the delivery. These underlying business models are called "service-oriented models", where the product offer is no longer the equipment itself, but the contractual agreement to specific performances of intangible features like transportation volume, reliability, physical parameters like torque, power, cutting speed or other measurable parameters, which are taken as contractual basis between vendors and customers. This new business model, already used in the software oriented IT and in the CAM/CAD industries, has now found strong interest from the manufacturing companies as well. To ensure that these industries would come into a similar business model, it is required exact knowledge about the behavior of the goods, equipment or the systems, their usage conditions, early detection of upcoming failure modes or maintenance requirements and dependencies with the delivered systems. Additionally, it is required a new way of engineering and design, called "functional engineering", in which a large quantity of data stream is used for validation, behavior prediction, simulation and more in general, will be produced in all phases of the product life cycle. These should be properly analyzed and intelligently managed.

Most important it will be the collaboration support, for improving the product and design life cycle and achieving better decision-making. As systems and products become more complex, and often offered by aggregation of companies (virtual enterprise), experts and engineers of the extended enterprise need to collaborate to manage integration and communication of the subsystems they design and develop.

Parallel to this, users, clients and stakeholders are becoming increasingly often included in various phases of a product life cycle, including design, user feedback, innovation and improvement cycles. Collaboration in the product's life cycle can be supported by suitable technologies.

The SmartVortex project (cf. www.smartvortex.eu) addresses these topics providing a technological infrastructure consisting of a comprehensive suite of interoperable tools, services, and methods for intelligent management and analysis of massive data streams to achieve better collaboration and decision making in largescale collaborative projects concerning industrial innovation engineering. It consists of a number of software components for capturing, processing and visually query high volume of data streams, together with collaboration and decision making support tools. In particular the infrastructure includes a (i) Data Stream Management System (DSMS), responsible of the access to the data streams and their processing, a (ii) Social Network, that basically realize a cross-organizational middleware which automatically connect organizations seamlessly while still protecting the intellectual property of the partners and minding the different company policies and the (iii) Collaboration and Decision Making Suite, playing a key role in helping the users in the collaborative work regarding data monitoring, data analysis and visualization.

In the following of this paper, we aim at introducing the main components of the Collaboration and Decision Making Suite, in particular (i) Elgar (cf. Section 2), a collaborative workspace supporting the elastic collaboration concept, and a standard interface to realize the integration of groupware tools, and (ii) RegalMiner (cf. Section 3), capable of capturing, monitoring, processing and visualizing collaboration data streams generated from users participating a collaborative process that exploits the elastic collaboration approach in order to evaluate individual and group performances by analyzing the generated activities and the produced text contributions.

Elgar

In complex design and engineering phases, different tools and techniques are useful at different point in the design. However, groups of engineers often do not have the knowledge to choose or select effective tools and techniques for specific situations. Elgar (ELastic GroupwARe) [START_REF] Janeiro | Supporting elastic collaboration: integration of collaboration components in dynamic contexts[END_REF] addresses these problems implementing different coordination mechanism to support groups in collaborative tasks; it provides structured and unstructured coordination.

The major goal of implementing coordination mechanisms in groupware systems is to support users to coordinate task dependencies to accomplish a shared task. On the one side (structured), coordination mechanisms that are highly specified and routinized processes are needed; this allows experts in a certain domain to define processes and structures to support and guide group participants. For example, a software release control system follows a pre-defined process of activities to release new versions of a software product, e.g., bug repair, application testing and final release compilation activities [START_REF] Ellis | Person-to-person processes: Computersupported collaborative work[END_REF]. On the other side (unstructured), coordination mechanisms that are highly unspecified and dynamic processes, are needed in order not use rigid structures or processes to guide group participants in their tasks. Instead, they relies on awareness mechanisms that allow for an informal understanding of the activities of others group participants [START_REF] Dourish | Awareness and coordination in shared workspaces[END_REF]. For example, a spatial visualization of programmers' activities in their development environment and within a project's source code repository system can improve the sense of awareness in a team [START_REF] Biehl | Fastdash: a visual dashboard for fostering awareness in software teams[END_REF].

Some groupware systems address more than one part of the coordination spectrum and provide mechanisms from both extremes. In such systems, the decision on a specific coordination mechanism is delegated directly to groups of participants, according to the situation they handle [START_REF] Bernstein | How can cooperative work tools support dynamic group process? Bridging the specificity frontier[END_REF]. For supply chains, Bernstein proposes a system that implements different coordination mechanisms to enable collaboration between users [START_REF] Bernstein | How can cooperative work tools support dynamic group process? Bridging the specificity frontier[END_REF]. The system implements four mechanisms: guidance through scripts, planning based on constraints, monitoring constraints and context provision. Each mechanism supports users to overcome natural unexpected obstacles from the disruption of a computer supply chain, from pre-defined supply chain processes to exploration of context information. Elgar was implemented as an extensible web-based groupware that provides a modular infrastructure to integrate software components. In Elgar, such components are called Elastic Collaboration Components (ECCs). They represent web-based components, inspired on portlets. Developers need to implement ECCs according to the architecture described in [START_REF] Janeiro | Supporting elastic collaboration: integration of collaboration components in dynamic contexts[END_REF] to use them as part of Elgar.

Currently four ECCs have been realized: V2QT, CollPad, Rating and CollPad Categorizer. The Visual Query and Visualization Tool (V2QT) [START_REF] Malagoli | Visual query specification and interaction with industrial engineering data[END_REF] is an ECC that enables users to visually pose continuous queries (CQs) over machine sensor measurements. V2QT handles query requests and responses and displays results in a time-oriented graph. Users gain insights, through the V2QT, over possible anomalous equipment behaviors, e.g. the main hydraulic pressure is above a threshold.

Collaborative Notepad (CollPad) is a multi-role ECC that allows its users to share their ideas and create text structures, based on lists, collaboratively. The component supports engineers in documenting ideas of possible problems, solutions for given problems, description of consequences for both problems and solutions, and to sketch action plans for a machine's diagnosis process. Such component is important to create collaborative documentation during a diagnosis scenario. The component allows users to define the topic of a certain discussion and provide contributions focusing on the topic. A contribution can either be a category or ideas. Users may combine the CollPad with the V2QT to annotate results of a graph at a particular moment.

Rating is an ECC that allows a user to rate a set of pre-selected contributions from CollPads. Users of Elgar drag contributions from CollPad and drop them in the Rating, creating a dynamic list of contributions collaboratively that will be compared by users through rates. In this component, users can rate the elements of a list according to a scale of five points. The idea is that the highest rated element should be considered for further attention of the group. The rate of contributions is calculated as the average of all rates provided by all participants.

CollPad Categorizer is an ECC that summarizes all discussions in a collaboration session. This component organizes discussions in a list. Each list entry represents a discussion topic, called category, and is automatically associated to a CollPad ECC. Users can contribute to a category by opening the associated CollPad adding, removing or editing content. In addition to the list of categories, users can classify the list of categories with a topic, called issue, which describes the goal of the CollPad Categorizer.

Currently Elgar supports two coordination mechanisms: ad-hoc and prescribed coordination mechanisms.

• Ad-Hoc Coordination Mechanism. The ad-hoc coordination mechanism implements a possible type of unstructured coordination, in which computermediated collaboration within a group naturally emerges and is not guided by prescription. It supports groups that do not need guidance or prescribed coordination during collaboration. The ad-hoc coordination mechanism relies on the use of workspace awareness information to divide and synchronize tasks among group participants. In this mechanism, such information represents all group participants connected in a collaboration session and the ECCs in which participants work. Through such information, participants may create an understanding about activities of others, facilitating their coordination to achieve a common goal, such as in [START_REF] Biehl | Fastdash: a visual dashboard for fostering awareness in software teams[END_REF]. In addition, this mechanism enables participants to share ECCs with each other to collaborate for content production. Participants can connect to ECCs opened by other participants, provided by workspace awareness information, sharing its content automatically. • Prescribed Coordination Mechanism. The prescribed coordination mechanism implements a possible type of structured coordination in which professional facilitators plan the sequence of phases and activities. This coordination mechanism guides group participants on the usage of elastic collaboration components. Guidance is described as a collaboration process, which represents a sequence of collaborative phases, each associated with a set of ECCs, and explicit instructions for the phase. In the prescribed coordination mechanism, the collaboration process is described at design time and coordinates the collaborative activities, executed by a group. It is based on process-aware systems [START_REF] Dumas | Process-Aware Information Systems[END_REF], in which processes are described to coordinate users in accomplishing established goals. The emphasis of the prescribed coordination mechanism is to provide guidance to users in collaborative activities through components, and not to require a facilitator to enable and disable components at specific moments [START_REF] Ellis | Person-to-person processes: Computersupported collaborative work[END_REF].

Regal Miner

RegalMiner is a collaboration support software tool that is capable of capturing, monitoring, processing and visualizing collaboration data streams generated from users participating a collaborative process, in order to evaluate individual and group performances by analyzing the generated activities and the submitted text contributions, to provide automatic notifications to a third-party facilitator or expert users in case typical meeting problems are detected and to supervise the meeting events to enhance the effectiveness of the current meeting session. The analysis and processing of activities and user contributions is demanded to text mining techniques [START_REF]Mining Text Data[END_REF]. RegalMiner adopts a modular approach that mainly consists of five components:

• The Fetcher module is responsible for the communication layer between the groupware platform that outputs the collaboration data streams (i.e., Elgar) and for the storage of all the retrieved information.

• The Reasoner module coordinates the Sentiment Analysis and the Keyword

Extraction modules along with the execution of several subtasks: o sampling the events extracted from the collaboration data stream, according to some predefined intervals that a user, in the role of meeting coordinator, can configure according to the expected time length of the ongoing collaborative process; o processing of sampled events in order to obtain some basic meeting indicators as Group Participation Rate, Individual Participation Rate, Distribution of Contributions, Number of Contributions, Number of Interactions that provide support for the detection of the levels of attention, interest, discussion and negotiation. The mentioned meeting indicators help diagnose typical meeting problems such as the Dominant Spaces syndrome that occurs when the formation of independent subgroups prevailing on other participants increases the levels of frustration and insulation. Another meeting problem that can be detected by the monitoring of these meeting indicators is the Sleeping Meeting syndrome that is caused by the lack of mental and physical energy thus decreasing the effectiveness of the overall collaborative process; o cycling the execution of the Sentiment Analysis module that processes the sampled events and returns scores for some high-level meeting indicators as Group Sentiment Analysis Score and Individual Sentiment Analysis Score. These meeting indicators relates to the level of interpersonal agreement or disagreement and individual positioning of participants involved in the collaborative session. Low levels of Sentiment Analysis indicators help to diagnose the Multi-Headed Beast syndrome, that occurs when there is no agreement on the agenda and confusion about the problem-solving strategies and participants interrupt or attack each other. Another typical meeting problem that can be detected by monitoring the mentioned scores is the Feuding Factions syndrome caused by internal power struggles that push participants against each other assuming an aggressive body language; o starting the execution of the Keyword Extraction module whenever the current meeting session has finished or having a break, thus returning the set of Extracted Topics for each participant. The extracted keywords can be associated with the topics and ideas argued during the meeting discussion, especially in brainstorming stages. The results of this process analysis provides insights about the levels of convergence/divergence towards a common solution, idea discussion and idea distinction that can help the early detection of several meeting problems such as Multi-Headed Beast, Dominant Spaces, Sleeping Meeting as already been discussed and Recycling syndrome caused by misunderstandings about the problemsolving processes thus resulting in a lack of consensus among participants that on the consequence start repeating the same arguments and reuse older solutions instead of generating alternative ones; • The Sentiment Analysis module is in charge of processing the contributions that participants submitted during a meeting session. In particular, it returns the Group Sentiment Analysis Score and Individual Sentiment Analysis Score. It implements a dictionary-based approach built on SentiWordNet [START_REF] Miller | WordNet: An on-line lexical database[END_REF][START_REF] Esuli | SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining[END_REF] that has proven to be efficient in terms of computational effort, especially in the case of repeated executions that are the requirement for a online analysis. The Sentiment Analysis module retrieves the collection of contributions for each participant and pre-processes the terms from extracted sentences, then detects terms that imply sense inversion as "no", "not", "don't", etc. and finally looks up into the dictionary to obtain scores for single terms, that are subsequently combined with other scores of terms in the sample of the collaboration data stream. A negative user score near -1 implies that the participant feels uncomfortable, isolated, in disagreement or in contrast with the rest of the participants, which can be one of the symptoms of the Multi-Headed Beast, Feuding Factions or Dominant Spaces syndromes. On the other hand, a positive user score near +1 indicates a user that feels positive, in agreement and an active subject of the group participating the collaborative process. It also reveals a high level of acceptance of group ideas, with a numeric majority of pros in contrast with a numeric minority of cons.

Careful attention has to be provided to neutral user scores near 0 that may imply hidden issues that may arise in future phases of the meeting session, since participants that fear changes or are under the influence of a subgroup may not express their real opinions. A facilitator or an expert user is required to thoroughly analyze and understand individual behaviors that happened in the previous stages of the collaborative process and subgroups compositions that may have affected the direction of the decision-making process in order to avoid future meeting problems. • The Keyword Extraction module is responsible for the automatic discovery of topics from submitted contributions, producing a set of keywords with their corresponding relevance score for each participant. The resulting sets represent the Extracted Keywords (or Topics) indicator that can be analyzed to identify subgroups compositions or isolated participants that may be the symptoms of the Feuding Factions or the Dominant Spaces syndromes that would decrease the effectiveness of the collaborative process. Furthermore the Extracted Keywords indicator reveals the level of idea generation, discussion, the level of similarity between proposed solutions or the level of convergence/divergence towards a common problem-solving model, as discussed in the previous chapters of this document. The Keyword Extraction module is coordinated by the Reasoner module that starts its execution once the coordinator selects the option to stop the fetching phase in case the meeting session has finished or having a break, since partial or none contributions would produce unreliable relevance scores. In fact, ideas can be developed during all the stages of the collaborative problem-solving process, either during the first phases or right in the middle of it. Extracting topics from single samples of the collaboration data stream would not consider concepts and notions that can be discussed in the subsequent stages of the meeting session. The natural approach is to examine the linear evolution of the collaborative process and not only the collections of fragments that represent the discrete samples generated from the Reasoner module. The implemented supervised approach used by the module takes advantage of a large training set constituted by almost 300 scientific articles from various disciplines, including Computer Science, Mechanical Engineering, and Electrical Engineering RegalMiner in order to amplify the end-user cognition of the ongoing collaborative process. All the visualization are characterized by some common features such as interactive live updates, zoomable area chart, animations, pop up tooltips for detailed information and different colors to distinguish different series.

Concluding Remarks

In this paper, we have outlined the main components of a Collaboration and Decision Making Suite specifically tailored to virtual enterprises adopting service-oriented business models and functional engineering, in which the ability to collaboratively manage huge data streams is a key factor. The platform is currently being validated in the context of an EU project by real users in the manufacturing industries, and preliminary feedbacks show the usefulness of the proposed tools, in particular the possibility to manage ad-hoc collaborations and to monitor and mine the collaborations themselves.

Fig 1 .

 1 Fig 1. The visualization of Elgar running the prescribed coordination mechanism.

  (a) distribution of contribution visualization (b) Spline chart sentiment analysis visualization (c) Participation rate visualization (d) Keyword extraction visualization

Fig 2 .

 2 Fig 2. Examples of RegalMiner visualizations • The Web Visualization module provides a computer-supported, interactive, visual representation of the data collected, managed, analyzed and processed by

Acknowledgments. This work has been supported by the FP7 IP project SmartVortex -Scalable Semantic Product Data Stream Management for Collaboration and Decision Making in Engineering. The authors would like to thanks Emanuela Bauleo and Serena Carnevale for the realization of the V2QT ECC.


Ali Ismail
email: ali.ismail@tu-dresden.de

Yaseen Srewil
email: yaseen.srewil@tu-dresden.de

Raimar J Scherer
email: raimar.scherer@tu-dresden.de

Collaborative Web-based Simulation Platform for Construction Project Planning

Keywords: project planning, simulation, data interoperability, collaboration

This paper presents a process-based simulation toolkit CST and collaboration platform ProSIM to support planning of construction projects using simulation methods based on Building Information Modelling (BIM) and multimodel data exchange approaches. CST uses reference process models for construction activities and addresses data interoperability problems caused by the diversity of available CAD/BIM and project-planning software. The online collaboration platform connects design, planning, construction, and simulation experts together and enables an efficient communication and information sharing. This paper focus mainly on the collaboration and data interoperability aspects of CST and ProSIM in order to enable a wide adopting of simulation methods for planning of construction projects.

Introduction

Simulation methods have been used to optimize and improve the planning quality in many industry sectors like manufacturing and logistic since long time, and they are integrated as an essential part of the whole design and planning process. However, in the construction industry, the use of simulation methods for planning has not been widely adopted, it is limited to analyse and solve problems in an ad-hoc manner (simulation study or simulation-on-demand) at certain time point of the project life.

Simulation studies have been carried out in most cases through external simulation service providers on project basis and they are not a basic part of planning process. The application of simulation techniques for construction projects is a very promising but also a challenging field of research [START_REF] Lucko | Harnessing the power of simulation in the project management / decision support aspects of the construction industry[END_REF]. While great advances have been made in construction simulation over the past few decades, adoption by industry has lagged, for three potential reasons: simulation is not accessible, it cannot handle the complexity of modern construction projects, and the benefits are not immediately obvious [START_REF] Abourizk | A Construction Synthetic Environment Integrating Visualization and Simulation ConVR[END_REF]. Creating reliable and reusable simulation models is very complex, combined with high costs of software licenses and personal training for in-house simulation. Thus, providing convenient simulation tools and web-based collaborative platforms with lowcost entry is crucial to promote a wider adoption of simulation in construction industry.

This research work, which is partly developed in the project Mefisto "Management, Leadership, Information and Simulation in Construction" (http://www.mefisto-bau.de), aims to make a paradigm shifting of traditional simulation studies to be an integrated part of the planning process for construction projects. The main contributions in this research are: (1) Using formal reference process models based on BPMN to describe the construction and logistic activities, (2) Integration of a multi-model approach to exchange structured project data with the simulation environment to enhance data interoperability, (3) Implantation of a construction simulation toolkit CST and a collaboration platform ProSIM. CST is a process-based discrete event simulation toolkit for construction project planning [START_REF] Ismail | Process-based simulation library for construction project planning[END_REF]. It aims to accelerate the process of creating simulation models for production and logistic operations. Simulation models built with CST will help to verify the feasibility of a given schedule against a combination of different resource constraints or different building design alternatives. It aims to improve the quality of projects planning by reducing the total duration and cost of construction projects and improving the utilization rates of resources.

The target of this research is to promote a wide adoption of simulation methods to be an integrated part of the planning process for construction projects through the whole project life cycle. In this approach, simulation models will be created once during the early design phase and accompany the project progress from primary design and planning phases until the end of the construction work.

Process-based and Multi-Model Data Exchange Simulation Approach

Construction is teamwork and successful team operations rely on collaboration. Because of the complexity of the construction industry, the multiple phases of the construction project lifecycle, the involvement of multidisciplinary teams (including owners, architects, consultants, engineers, contractors, sub-contractors, and suppliers), and the use of heterogeneous software and hardware systems/tools, systems integration becomes an important prerequisite to achieve efficient and effective collaboration [START_REF] Shen | Systems integration and collaboration in architecture, engineering, construction, and facilities management: A review, Eabling Technologies for Collaborative Design[END_REF].

The complexity of simulation models for construction projects and the increasing volume of information raise the need to adopt new collaboration and project data exchange methods. As the project size grows, the number and size of teams will increase. An interoperable and smooth data flow between planning and simulation experts and their tools is a key factor to reduce the time and efforts required to collect and update project data regularly. In our approach we use reference process models to manage the knowledge in the construction domain collaboratively and a new method to exchange project data based on multi-model method.

Reference Process Models

Reference models are generic conceptual models that formalise recommended practices for a certain domain [START_REF]Application reference models and building blocks for management and control (ERP Systems)[END_REF]. As the knowledge in every domain is keep evolving, the reference models will go through the known approach "discover, model, evaluate, and optimize" so new models will be added or existing models will be improved and detailed with time (Fig. 1). Therefore, a continuous collaboration between planning, construction management and simulation teams is very important to validate and integrate the feedbacks during the construction phase and embedded them correctly in the reference models. The reference process modelling method allows identifying, capturing, documenting, improving and sharing knowledge about the best practice of construction processes and their related information In our approach, there is a clear separation between the core simulation engine and the process models for the application domains. Business Process Modelling and Notation (BPMN) models are used to capture and describe the logic of production and logistic operations and to transform them directly into simulation models.

Extension elements are introduced in order to define resource requirements and durations of tasks. Each activity or task inside the project schedule can be linked with a reference process model during simulation. In this way, the process models define the logic and level of details of simulation models. One needs to carefully examine every detail of the construction process and identify the major events and processes that will be presented in the simulation model in order to create a reliable simulation model [START_REF] Aouad | nD modelling for collaborative working in construction[END_REF]. The graphical representation of BPMN models makes models easy to understand and the formal specifications in XML allows transforming process models into simulation models automatically. The BPMN reference process models can be imported into a process repository in the simulation mode (Fig. 2). The process repository includes reference process models for the best practices of various construction processes that are reusable across different construction projects and their data definitions of resources and productivity factors.

Multi-Model approach to exchange projects data

Multi-model project planning is a paradigm shift from the building-centric approach, which tries to add and link all project data with building models toward an equivalent interlinked data models using special link models. The multi-model method offers solutions to structural problems of nD modelling in construction information processes [START_REF] Akhavian | Dynamic Simulation of Construction Activities Using Real Time Field Data Collection[END_REF]. A multi-model container comprises several data files and includes, besides the meta-information about the container content, several application models as well as link models [START_REF] Schapke | A distributed multi-model based Management Information System for simulation and decision making on construction projects[END_REF]. It allows the combination of heterogeneous application models from different domains and various data formats. Inside multi-model containers link models bind the application models together. The link models specify the relationships between items from different data models. Multi-model containers can be used to exchange associated models among the project stakeholders by a common format. In their entirety the multi-models on a construction project open up a multi-dimensional information space of interdependent application models that can be independently processed by the project participants. Each participant has the opportunity to produce new application models on his/her own responsibility and interlink them with existing models. Depending on the situation these newly created multi-models can be maintained locally or published project-wide as a basis for further planning and controlling tasks. In comparison to the often pursued integration of project information in central project databases or product model servers, this approach distributed model-based collaboration represents a paradigm shift [START_REF] Schapke | Multi-models: New potentials for the combined use of planning and controlling information[END_REF]. Multi-model approach is integrated with CST through providing import/export interfaces.

Collaboration Platform ProSIM

ProSIM is a web application designed and implemented especially for CST to work as a communication and collaboration layer between the simulation team and other design and planning teams. The main functions provided by ProSIM are:

• Share project data and keep them up-to-date: BIM models, project schedules, process templates, productivity factors can be updated at any time to reflect the actual state of the available information. • Import and view the simulation inputs of various data models and multi-model containers directly inside the web browser. • View and evaluate simulation results through online viewers to display results inside a web browser or download them using standard formats • Manage construction process templates.

• Manage productivity factors, definition of resource requirements, and task duration of construction activities. • Add, edit and run simulation experiments remotely.

Simulation models and their inputs and results will be available and re-runnable anywhere and responsive to any data update. The visibility of all input data and simulation parameters helps to identify errors and leads at the end to a better understanding among the planning and simulation teams and offers a high level of transparency and trust. Available API interfaces beside the simulation database connectivity make it possible to build integrated data management services around the simulation core components and offer it as Software as a service (SaaS).

Web-based simulation has many benefits over the classical desktop simulation systems, main advantages of using a web-based simulation tools are [START_REF] Byrne | A review of Web-based simulation and supporting tools[END_REF]:

(a) Collaboration: Communication and interaction are one of the essential factors to achieve a successful simulation project, (b) Cost reduction: traditionally the starting investment for a typical simulation environment or external simulation consultant are on a high level, the web-based interface allows new business models for the use of simulation services, which can be rented for an interval of time, this resulting in savings in terms of possible previous prohibitive factors of time and cost, (c) Wide availability: A Web-based simulation application can be used from anywhere in the world with an Internet connection, (d) Integration and interoperability: A Web-based tool can integrate and interoperate with both existing and future Web-based applications.

The main ProSIM collaboration features in the different project phases are: Pre Planning phase:

• Share and manage information about reference process models, productivity factors for workers and machines, and resource definitions of construction activities.

• Define the minimal requirements of information in BIM models and modelling guidelines based on the reference process models and scope of simulation study.

Design and planning phase:

• Provide import interfaces and online viewers for BIM and project schedules.

• Visualize and archive results for each simulation scenario.

• Change simulation parameters and run simulation scenarios remotely.

Construction phase:

• Collect real-time data from construction site and update the simulation model according to the real project progress and the changes in the construction site layout. • Update estimated productivity factors and duration formulas based on actual duration of executed tasks. • Improve reference process models continuously.

The real-time data collection aims to support project planning and simulation with accurate feedback during project execution phase. For this purpose, a developed solution based on RFID technology is envisioned [START_REF] Srewil | Effective construction process monitoring and control through a collaborative Cyber-Physical approach[END_REF]. This solution proposed a mechanism for semi-automation data acquisition on construction site throughout coupling the BIM/planning models and the physical construction using cheaper passive RFID tags. From collected and validated data the construction process progress can be derived and therefore as-built data extracted. This information can be send as inputs data to the ProSIM platform using an API interface leading to short-term simulation loops.

Prototype Implementation and validation

This section describes briefly the system architecture and the prototype implementation of ProSIM.

Platform Architecture

The platform architecture consists of five main modules (Fig. 4): (1) Simulation engine (2) Web server (3) Simulation database (4) BIM data server (5) ProSIM web application.

ProSIM; Technical Implementation

The ProSIM platform is designed as a Web-based system and implemented using the framework Ruby on Rails and the Web application is deployed via the Apache web server. The server-side data are stored in a SQLite database. The BIM and 4D simulation results models can be shown using a WebGL viewer based on the JavaScript 3D library "threejs" and Collada format. Ruby on Rails is suitable for rapid development; it emphasizes the use of well-known software engineering patterns and principles, such as active record pattern, and model-view-controller. The technical implementation has resulted in a rich web application with a REST API interface.

Validation and test cases

The CST simulation toolkit and the prototype of ProSIM platform were tested and validated based on two construction projects (multi-storey office building and an airport terminal). The easy access of projects data and simulation models through the online platform encouraged and enabled a collaborative work between different planning and simulation experts from different firms and helped to verify all input parameters and simulation results effectively. Some snapshots of a test case simulation model inside ProSIM are shown in Fig. 5. Simulation models of test cases can be accessed at: http://bci52.cib.bau.tu-dresden.de:3000

Conclusions

The target of this research is to promote a wide adoption of simulation methods to support construction project planning. Systems integration and collaboration are

Multi-Model Navigator

Resources monitor 3D/4D Viewer Drawing Panel believed to be the key enabling technologies that drive the construction industry in improving productivity and efficiency [START_REF] Shen | Systems integration and collaboration in architecture, engineering, construction, and facilities management: A review, Eabling Technologies for Collaborative Design[END_REF]. This paper presented the integration between a process-based simulation toolkit and a collaborative platform. It discussed also the use of reference process models to capture and manage knowledge in construction domain and the use of multi-models improve data interoperability.

Fig. 1 .

 1 Fig.1. Reference process models life cycle

Fig. 2 .

 2 Fig.2. Transformation of reference process models to simulation modules

Fig 4 .

 4 Fig 4. System architecture and data flow

Fig. 5 .

 5 Fig.5. A screenshots from a test case simulation model inside ProSIM

Acknowledgments. This research in this paper was enabled by the financial support of the German Federal Ministry of Education and Research (BMBF), Department of ICT under contract n° 01IA09001A, which is herewith gratefully acknowledged.


Cesar Garita

Giannina Ortiz
email: gortiz@itcr.ac.cr

Towards a Workflow Management Approach for Health Monitoring of Bridges

Keywords: Structural health monitoring, bridges, workflow, data integration

In brief, Structural Health Monitoring (SHM) refers to the storage and analysis of large amounts of sensor data associated to specific variables that indicate the condition of a civil structure, e.g. a bridge on a national highway. SHM analysis tasks typically involve the integration of different tools which may be highly heterogeneous, autonomous and physically distributed among different organizations.

This paper introduces a scientific workflow management approach to model and execute strategic SHM queries that require proper integration of data generated by different software components.

Introduction

Structural Health Monitoring (SHM) is a process consisting in the periodic recording of data generated by sensors associated to parameters that adequately reflect the performance and the condition of a civil structure [START_REF] Glisic | Fibre Optic Methods for Structural Health Monitoring[END_REF]. This raw data is then processed to obtain valuable information for planning of maintenance activities, increase safety, verify hypothesis about the structure and, in general, to expand the knowledge about the behavior of the structure being monitored. Proper monitoring of civil structures such as bridges contributes to prevent the socio-economic consequences that may be caused by a damaged structured.

In the particular case of bridges, SHM systems should ideally support integrated decision-making processes through functionalities such as: user friendly web interfaces, GIS (Geographic Information System) services, distributed monitoring units, data management features, historic knowledge bases, expert systems, and integration of bridge inventory systems [START_REF] Wenzel | Health Monitoring of Bridges[END_REF].

Although, there are some systems available in the market offering integrated solutions with many of these capabilities, in practice, individual institutions working on SHM have developed different systems and used a wide variety of tools depending on their specific objectives and approaches (see [START_REF] Küng | Utilizing Ontologies to Integrate Heterogeneous Decision Support Systems[END_REF]). For an effective development of SHM policies, different institutions and professionals including civil engineers, university labs, transportation ministries, and local government units (municipalities), need to share and analyze large amounts of SHM-related information generated by heterogeneous and distributed systems and tools. This scenario lays out specific information integration requirements that can be met by Collaborative Networks (CNs) framework and in particular, by workflow management components.

This paper explores some SHM scenario requirements and introduces the application of scientific workflow management technologies for information integration in this field.

Related work

There exist several studies regarding the state-of-the-art of SHM of bridges (see [START_REF] Ahlborn | The State-of-the-Practice of Modern Structural Health Monitoring for Bridges: A Comprehensive Review[END_REF], [START_REF] Glisic | SHM process -lessons learned in 250 SHM Projects[END_REF]). These works summarize established and emerging technologies, practices and projects in the field. Other reports focus mostly on commercially available systems and their functionalities for SHM support, including specific systems such as BRIMOS and SHM Live [START_REF] Dong | Bridges Structural Health Monitoring and Deterioration Detection -Synthesis of Knowledge and Technology[END_REF], [START_REF]Brimos Project[END_REF]. These systems offer a variety of services and functionalities for on-line monitoring that come "off-the-shelf" with a proprietary infrastructure. Other specific approaches for data management systems in SHM can be found in [START_REF] Koo | SHM Data Management System Using MySQL Database with MATLAB and Web Interfaces[END_REF], [START_REF] Tas | Bridge Sensor Mart: A Flexible and Scalable Data Storage and Analysis Framework for Structural Health Monitoring[END_REF], [START_REF] Inaudi | Database structures for the management of monitoring data[END_REF]. Most of these initiatives are based on a centralized relational database approach for gathering, storing and analyzing data.

On the other hand, the Collaborative Network (CN) paradigm has been successfully applied to similar complex domains (e.g. industrial manufacturing, tourism value-added service provision, virtual laboratories) in which different entities that are physically distributed, autonomous and heterogeneous, need to collaborate in order to achieve common goals based on computer networks [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF]. In fact, an early conceptual paper on Virtual Enterprises (VEs) mentions how this manifestation of CNs can be applied in a "building a bridge scenario" during the preparation of the call-for-tenders for a construction project [START_REF] Camarinha-Matos | The Virtual Enterprise Concept[END_REF].

Nevertheless, the application of specific CN models and tools in SHM, such as distributed business processes or workflow management techniques in order to define, execute and coordinate tasks that must be achieved by network entities, remains an open issue. In [START_REF] Küng | Utilizing Ontologies to Integrate Heterogeneous Decision Support Systems[END_REF], a ontology-based mediator system is proposed for semantic integration of different tools and systems in SHM; this mediator is seen as a "router" that suggests specific tools to the user following a general workflow (planning, monitoring, data preparation, evaluation). However, this approach uses this workflow as a reference and does not consider generic workflow management features.

In this paper, we present a novel approach for data integration based on generic definition, execution and monitoring of scientific workflows that support different data analysis scenarios in SHM. The proposed approach is being developed in the context of the e-Bridge 2.0 project as described in the following section.

The e-Bridge 2.0 General Approach

The main goal of the e-Bridge 2.0 Project -Integrated System for Performance Assessment of Bridge Structures, is to develop a prototype of a system for solving strategic queries regarding the performance of bridges. This project is carried out at the Costa Rica Institute of Technology (TEC) as part of an e-Science Program bringing together engineers from different schools including Construction Engineering, Electronics, Computer Science, Industrial Production Engineering and Forest Engineering. The main components to be developed or integrated within e-Bridge 2.0 include:

• Reliability model: a probabilistic model to predict structural behavior of bridges using variables that have an impact on potential structural failures. • Technical information system: integration of data from on-going national inventory of bridges and their technical characteristics. • Sensors systems: development of sensors networks for SHM of bridges to measure variables such as vibration, deflection and deformation. • GIS: web-based geospatial information system to properly assess environmental aspects associated to national bridges. • Information integration system: a single entry-point for users in order to access and analyze strategic information generated by other components. The implementation approach followed in the project promotes the use of existing open-source technologies and tools from multidisciplinary domains based on a peerto-peer communication architecture. This approach foresees integration of other escience tools regarding for instance: simulation environments, 3D visualization tools and high-performance computing clusters. Please notice that in this "open environment", each component may use different data models and software tools. However, in order to solve strategic queries regarding high-level SHM scenarios, information from these heterogeneous sources needs to be seamlessly integrated from a single interface for end-users. The following section provides some examples of these requirements and scenarios.

Requirements Analysis for Information Integration

In order to identify general functional requirements for the e-Bridge platform, two main kinds of workshops were organized:

1. Internal workshops. A first workshop was organized with the university colleagues working on the project in order to achieve a common internal perspective on the main requirements for the project. This workshop allowed the identification of main personas (user roles) and system functionalities from an internal point of view. 2. External workshops. Several other workshops were organized with external potential users of the platform including collaborators from the Ministry of Transportation, Ministry of Agriculture, Ministry of Health, National Road Council, municipality councils, and civil engineers.

These external workshops delivered new potential functionalities and allowed us to validate the results of the internal workshop. Some of the strategic queries that were identified through these activities include:

• Real-time failure detection based on different types of sensors.

• Validation of structural models (real vs. theoretical bridge models).

• Generation of bridge reliability rankings.

• Analysis of remaining lifetime for given bridges.

• Identification of failure tendencies by structure types.

• Lists of bridges by maintenance priorities.

• Alternative traffic routes in case of bridge failures.

• Vulnerability (risk) analysis of bridges according to environmental factors. This analysis allowed the identification of requirements for the general platform and more specifically for the Information Integration System. For this system, we concluded that there are two main types of strategic queries that need to be covered:

1. Predefined queries: solving specific strategic queries for high-level end users e.g. a minister who asks: which are the most important bridges that need urgent maintenance in the country? 2. Analysis queries: solving specific technical queries for experts and engineers that need to perform SHM tasks or queries of a more scientific / experimental nature, e.g. can this bridge support an 80-ton truck load at 60km/h speed? The first type involves common important queries that are relatively fixed or welldefined but that need to be solved through configurable integration of different components. For instance, in the case of "real-time failure detection", the following interaction sequence among platform actors can be outlined:

1. Sensors System detects a strain measurement value out of the normal range for a given bridge beam and sends an alarm in real time to the Information Integration System.

Information Integration System sends an alert to the Geographic Information

System so that the event is properly reflected in the GIS layers.

Information Integration System records the event in the bridge Technical

Information System. 4. Information Integration System sends an alarm SMS/email message to operators responsible for bridge maintenance.

Bridge maintenance operators access the Information Integration System

interface with a dashboard for global bridge monitoring, providing access to detailed information about the event.

Bridge maintenance operators enter the GIS from the Information Integration

System and analyze possible actions depending on the bridge location. Currently, the components involved in this scenario are managed by TEC, but in the near future, they may be managed by different organizations, for example: the bridge inventory may be managed by the Ministry of Transportation, the Geographic Information System may be hosted in the cloud by a GIS service provider, the bridge monitoring dashboard may be accessed by National Road Council, and the Information Integration System and the Reliability Model may be hosted at TEC or other university.

The second type above involves queries that relate to a flexible "problem-solving environment" (e.g. similar to a virtual laboratory) in which construction engineers need to experiment, test hypothesis and answer questions in different ways. An example of this kind of queries will be given in the next section. In both cases, queries require the integration of data from different tools that may be located in different organizations.

Based on the analysis both kinds of strategic queries, a scientific workflow approach is suggested to properly model and support these requirements. This workflow approach is described next.

Scientific Workflow Management Scenarios

According to the Workflow Management Coalition, a workflow is "the automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules" [START_REF]Workflow Management Coalition[END_REF].

Workflows represent a prevailing approach for service-oriented integration since they provide the "glue for distributed services, which are owned and maintained by multiple organizations" [START_REF] Barker | Scientific Workflow: A Survey and Research Directions[END_REF]. When workflows are applied to support large-scale scientific applications, they are called scientific workflows. These scientific workflows set forward specific challenges in relation to traditional businessoriented workflow management, such as the need to handle large amount of data, intense computational tasks, and more data provenance and "interactive steering" features for scientists and engineers [START_REF] Chen | On scientific workflows[END_REF]. Furthermore, workflow management technologies have been extensively used to support Collaborative Networks scenarios involving distributed, heterogeneous and autonomous entities [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF], [START_REF] Afsarmanesh | Workflow Support for Management of Information in PRODNET II[END_REF], [START_REF] Garita | Building a Virtual Laboratory for Scientific Experimentation in Molecular Biology[END_REF]. Certainly, other approaches for coordination of these entities are possible, including agent-based technologies, coordination languages, and GRID services. Nevertheless, the workflow-based architecture was chosen for e-Bridge since it represents a simple, common, flexible and user-friendly approach for service integration. In particular, scientific workflows tools allow the modeling and execution of SHM analysis tasks in a way that can be easily adopted by engineers and scientists from different disciplines with some technical training.

In this context, based on the requirements analysis presented in the previous section, a workflow management approach has been adopted for information integration in e-Bridge 2.0. To this end, several business process / workflow tools have been compared and evaluated including Bonita, Taverna, Bizagi, and jBPM among others. As a result of this evaluation, Taverna is being currently used to model and execute preliminary workflows in this project.

Taverna is a Java-based open-source workflow management system that offers a set of tools to define and execute scientific workflows [START_REF]Taverna Project[END_REF]. Taverna provides a desktop application that allows scientists and engineers to easily define and execute workflows without the need of advanced software development skills. Taverna workflows can also be edited and executed on the web and invoked from the command line in batch mode. Workflows are represented as directed acyclic graphs using a dataflow language called SCUFL (Simple Conceptual Unified Flow Language). Nodes in these graphs are called processors which can be regarded as mathematical functions mapping a set of input data to a set of output data. Processors can be connected through data links to achieve function composition. Finally, Taverna allows the integration of many different software components through SOAP or REST web services.

The proposed general architecture for the Information Integration System of e-Bridge 2.0 is depicted on Fig. 1. This figure describes how the system architecture supports analysis queries for engineers as well as predefined queries for end-users, using Taverna workbench and server tools respectively. In this way, e-Bridge workflows can be created and executed orchestrating web services related to other e-Bridge components and potential organizations.

The current prototype of system includes basic Taverna workflows which allows construction engineers to seamlessly orchestrate distributed web services to carry on sensor data acquisition, database storage and data visualization (see Fig. 2). In brief, the sample workflow presented in this figure basically collects sensor data as a file from a remote data collection unit (e.g. FTP server) associated to a bridge, processes the file, stores the data in the local database, and finally formats and exports data sets that can be visualized on a web browser (see Fig. 3).

Workflows can be properly edited by engineers by using other existing web services and connecting them accordingly in a graphical environment. Workflows can also be executed from Java applications that will offer strategic queries for SHM. Currently available web services have been developed using SOAP with Java on Ubuntu Linux operating system and PostgreSQL as database engine.

and Future Work

This paper described how a scientific workflow management approach can be applied to support information integration scenarios in the specific domain of structural health monitoring of bridges. In summary, through the workflow-based information integration component, e-Bridge 2.0 end-users will be able to solve general strategic queries as well as tailored experimentation/analysis tasks regarding structural monitoring of bridge behavior. The proposed service-oriented workflow approach allows the seamless integration of sensors, GIS, reliability model and technical data, which may be physically distributed over different organizations. Future work within the project includes the specification of more integration scenarios involving other components and organizations, development and integration of web-services according to these scenarios, and development of web applications.

Fig. 1 .

 1 Fig. 1. General architecture of the Information Integration System.

Fig. 2 .

 2 Fig. 2. Sample partial Taverna workflow for sensors data analysis.

Fig. 3 .

 3 Fig. 3. Visualization of sensor data for bridge vibration variables.

Acknowledgements

The authors of this paper wish to acknowledge the valuable contributions from all colleagues and partners of the e-Bridge 2.0 project.


Lai Xu

Paul De Vrieze
email: pdvrieze@bournemouth.ac.uk

Lingling Wei
email: lwei@bournemouth.ac.uk

Supporting Interoperability of Virtual Factories

Keywords: Collaboration, Interoperability, Virtual Factory, Virtual Manufacturing

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

One of the most important and visible trends in recent years for the manufacturing industry is towards the use of ICT to enable intelligent manufacturing [START_REF] Effra | EFFRA Research Priorities[END_REF] [START_REF]GUIDE TO SOCIAL INNOVATION[END_REF]. ICT enabled intelligent manufacturing has been identified as a key aspect in supporting the European manufacturing industry in the challenging transition from post-crisis recovery to European STEEP (Social, Technological, Economic, Environmental and Political) sustainability and regain competitive advantage in the global market competition [START_REF] Effra | Factories of the Future 2020 Roadmap[END_REF]. ICT (and especially intelligent manufacturing) in this context is not only important for design, production, testing, distribution and recycling, but it is also crucial in supporting changing business trends. Intelligent manufacturing has seen massive adoption in large corporations, promising to close the gap between what companies or factories require and what IT is able to deliver. Many EU initiative programmes and projects, such as FI-PPP1 , FI-WARE2 , FInES 3 , FITMAN 4 , and MSEE 5 , industrial advisory groups and research associations are investigating a more flexible IT infrastructure that is able to react to business changes more quickly than the classic monolithic IT manufacturing systems [START_REF] Effra | Roadmap 2010-2013 -Digital Technologies -ICT-enabled Intelligent Manufacturing[END_REF] [START_REF] Fines | FInES Research Roadmap[END_REF].

New ICT technologies and collaboration applications are integrated into traditional manufacturing practices and processes. For example manufacturing 2.0. Manufacturing 2.0 has been conceptualised as a system that goes beyond the factory floor; it allows paradigms such as "manufacturing as an ecosystem" to emerge. An extension of this paradigm, virtual factories, is one of the central concepts in providing the foundation of future manufacturing. A virtual factory allows the amalgamation of manufacturing resources to create timely, demand driven product lines. A virtual factory can also be seen as a specific virtual enterprise which focuses on manufacturing.

Surveys, previous research, and experience shows that traditional manufacturing, supply chain and business systems are primarily used for internal integration. Sometimes this integration is with a fixed number of external partners (for example traditional supply chain management systems). Only very few companies/factories can offer access to their (internal) services to a limited set of customers/suppliers. Even then these integrations are closed environments. It has been increasingly recognized that this is insufficient for the future.

Forming a virtual factory, collaborative partners need to communicate, cooperate, collaborate, or interoperate with others. These partners who can be located anywhere, with day to day collaboration coordinated over the Internet. Interoperability is essential to go beyond the small sets of limited interoperability to a dynamic environment with cost-effective, efficient and deep integration between multiple partners. This integration enables these partners to share information and services as well as to form collaborative processes.

In this paper, we look at how to use ICT to facilitate companies, organisations, and factories in interacting with each other to form supply chains and business ecosystems. More specifically, we look at the role of semantic discovery and interoperability in facilitating virtual factories, a key aspect of future of manufacturing.

The paper first looks at the technical challenges in manufacturing interoperability. Next, related issues to manufacturing interoperability are discussed. We provide the architecture of a manufacturing interoperability platform. Finally, the paper reviews related works and concludes with a future research direction.

Interoperability Issues and Enablement

The increased reusability of manufacturing systems leads towards globally interoperable factories. These are then able to provide services and develop products anytime and anywhere, independent of the technologies, culture or language in use at the different production sites [START_REF] Effra | Factories of the Future PPP Strategic Multi-annual Roadmap[END_REF]. Real-world virtual factories combine the resources of pre-existing organisations. Those resources are generally supported by existing legacy systems that cannot be changed for the purpose of the creation of a virtual factory. This means that an interoperability platform is a hard requirement for realising virtual factories. The interoperability platform further enables collaboration.

In the "factories of the future roadmap" [START_REF] Effra | Factories of the Future 2020 Roadmap[END_REF] the European Commission sets out its strategy on the future of manufacturing. We have extracted a number of interoperability issues that have been identified in this document. These issues form the focus of this paper. The first issue is the need to realise a supporting infrastructure for manufacturing business Webs (MBW). Nowadays the Internet is a major driver in collaboration with very significantly reduced costs. The expectation is that collaborative supply processes are location independent, and work in cloud-like ways. The end users of the supply network perform end-to-end manufacturing services orchestration: covering activities of customer collaboration, collaborative service management, and collaborative manufacturing.

Second, to support rapidly evolving virtual factories it is needed to build commonly-used IT backend systems that form the backbone infrastructure for these factories. Such systems facilitate holistic representation, monitoring, and management of future (virtual) factories. A critical enabler of the implementation of a backend system of virtual factories is an integrated scalable and semantic model of manufacturing.

Third, enterprises are increasingly facing frequent design changes. It is essential for European manufacturing to have a high degree of reactivity in rapidly delivering new products and services based upon design changes. In this paper we look at how to increase this reactivity. This is in contrast to traditional research on supporting enterprise integration that focuses on supply chain aspects. To enable rapid change in the setup of virtual factories, potentially interrupting a long life cycle, a holistic approach is needed. A core aspect of the approach is an integrated vertical view on interoperation: not only the data level interoperation, but also service level or even process level interoperation.

To support the above mentioned requirements, a platform has to support interoperability on different inter-connected levels, namely a data/information level, a service level, and a process level.

• The data/information interoperation level is related to sharing and exchange of different data, documents, messages and content between different collaborating enterprises from different factory data resources. At this level, the data format, the semantics of contents and documents' structure are important for crossenterprise collaboration. • The service interoperation level is providing the capability to discover, aggregate, orchestrate and execute various services that have been independently designed and implemented. At service level, interoperability supports transparent composition and mediation among specified services in a cloud environment. • The goal of the process interoperation level is to integrate internal processes of an organisation with the partners' processes to generate cross-organisational business processes. Table 1 shows each level of interoperation and its enablement.

Interoperatio n Level

Interoperation Enablement Examples

Data/informat ion interoperation level

• Building an ontology or ontological models is a common solution to support interoperability.

MSE (Manufacturing System Engineering

) ontology [START_REF] Xie | When Social Software Meets Business Process Management[END_REF]; MASON (Manufacturing's Semantic Ontology) [START_REF] Xu | Incident Notification Process as a Service for Electricity Supply Systems[END_REF] • Building common virtual factory data model (VFDM) that consider as a shared meta-language providing a common definition of the content and intent of data within a virtual factory ecosystem.

Some

Process interoperation level

Process level interoperation defines synchronisation steps and messages and defines coordination and collaboration mechanisms for collaborative processes.

The Process Specification Language (PSL) standard [START_REF] Sun | CoBTx-Net: A model for business collaboration reliability verification[END_REF] specified a general ontology for manufacturing processes to exchange process information and knowledge. ANSI/ISA-95 [START_REF] Xie | When Social Software Meets Business Process Management[END_REF] is another international standard, which provide a consistent terminology and information models as well as consistent operation models.

Figure 1 provides an architectural overview of the interoperability platform. The overall architecture can be structured in to four parts: the repository parts (the low three repositories), sematic supporting part (the middle part), interoperability parts (the top three blocks), and manufacturing standards (the right part).

The bottom part of the architecture provides related manufacturing resources. The data repository/resources represent manufacturing data resources, data from assets, machines, sensors, objectives, and workers. The manufacturing asset service repository/resource denotes manufacturing related assets and services resources. The collaboration process repository stores collaboration capabilities in a business process model format.

Figure 1 Architecture of the Manufacturing Interoperability Platform

The middle part of the architecture provides semantic supports. All potential manufacturing related data, assets, services and collaboration processes need to be annotated using suitable methods. The semantic annotation provides a way to interpret the meaning of data, information or knowledge, which deals with data/information integration and consistency issues. Annotated data, information, or knowledge is foundation of supporting cooperation, collaboration and knowledge and information sharing around a virtual factory. The different semantic description languages describe both tangible and intangible assets of manufacturing services and manufacturing collaborative processes. The semantic description languages may comply with base upon existing languages, such as USDL (Unified Service Description Language). The semantic discovery methods contain a board collection of existing work on the dynamic discovery of semantically annotated manufacturing assets and services as well as collaborative processes.

The top part of the architecture supports interoperability. The data/information interoperability helps data/information transformation, which define the ability of sharing, aggregating or synchronising data or information across different partners within a virtual factory. The manufacturing asset service interoperability assists as service brokerage functionality to wrap service bundles or value-added services. The process interoperability brings collaboration capabilities within a virtual factory and deal with coordinating business processes.

The manufacturing standards part allows transforming textual rule of engagement, regulations of different countries, and industrial standards into computer understandable knowledge. This can then be used to ensure that new forming manufacturing processes or services comply with existing rules.

Related Work

Previous EU projects SOA4All, ATHENA, Super, and COMMIUS have discovered semantic interoperability which addresses standards based approaches, semantic technologies, architectures and frameworks, business vocabularies, modelling languages and methodologies. The above mentioned projects are not specific for manufacturing domain. Although the many design principles are adoptable, there are many specific issues for implementing interoperability in manufacturing domain. For example, service specification languages are not designed for manufacturing services. Manufacturing assets could be both tangible and intangible assets. To transfer different manufacturing assets into manufacturing services is certainly new for dealing with interoperability of virtual factory. Further manufacturing service clustering and discover methods are certainly different with general service clustering and discovery methods, a deep analysis is needed of characteristics of manufacturing services, which is critical for clustering services and optimising the service discovery process.

EU projects such as VFF, FITMan and MSEE are in the domain of virtual factories. There are still differences with our research perspectives though. None of these projects completely addresses end-user aspects, or setting up a useable BPaaS. Our work [START_REF] Xu | Interoperative end-user process modelling for process collaborative manufacturing[END_REF] on the area of interoperative end-user process modelling for process collaborative manufacturing and BPaaS (Business Process as a Service) [START_REF] Xu | Incident Notification Process as a Service for Electricity Supply Systems[END_REF] can certainly contribute to the new approach to support interoperability.

Paper [START_REF] Zöllner | Coperion 3D-A Virtual Factory on the Tabletop[END_REF] describes the development of a virtual factory application based on multi-touch interaction, high resolution projection technology and industry standards like X3D. Ding, et al. introduce how to using 3D technique to design and simulation of virtual factory layout [START_REF] Ding | An Interactive Layout and Simulation system of Virtual Factory[END_REF]. Paper [START_REF] Yang | Development of a prototype customer-oriented virtual factory system[END_REF] presents a structure and architecture of an integrated simulation method (ISM) to meet the requirements of virtual factory engineering (VFE) which combine CAD, VR and discrete event simulation techniques. Above mentioned ontologies can be used for supporting our vertical interoperability of virtual factories.

Conclusion

In this paper we discussed what is needed to provide interoperability in virtual factories. We identifie three different levels of interoperability, i.e. data/information level, service level, and process level. For each level, the potential issues and applicable technologies were analyzed and specified. We also provided an overall architecture for implementing such interoperability in the domain of virtual factories. The interoperability of the three distinguishing levels is aligned with the concept of service-orientation. Core scientific challenges remain on the semantic level specification and integrating three-level interoperability as one.

Table 1 .

 1 Interoperation and Its Enablement.

FI-PPP: https://www.fi-ppp.eu/

FI-WARE: http://www.fi-ware.org/

FInES: http://www.fines-cluster.eu/

FITMAN: http://www.fitman-fi.eu/

MSEE: http://www.msee-ip.eu/

Acknowledgments. This work is made possible by the support of the Natural Science Foundation of China (NSFC) under Grant No.61150110484, ESSENTIAL: Enterprise Service deSign based on ExistiNg software Architectural knowLedge, the National Basic Research Program of China under Grant No. 2014CB340404, and FIF Strengthening Service Computing Research in Bournemouth University, UK.