Marco Formentini
email: marco.formentini@uniud.it

Michela Lolli
email: michela.lolli@uniud.it

Alberto Felice De Toni
email: detoni@uniud.it

Designing and Implementing a Web Platform to Support SMEs in Collaborative Product Development

Keywords: Product development, Collaboration, Web platform, SMEs. 1 Research context

Research on the product development process lacks of investigation on the aspects related to its potential integration in a web-based collaborative environment, especially when considering the practical needs of Small Medium Enterprises (SMEs).Our study aims at investigating how is it possible to design and develop a web-based platform aimed at supporting SMEs in the product development process. On the basis of a thorough literature review and a mapping of the existing platform and tools available in the design domain, this paper describes in detail the development of a ICT platform, called iCommunity, which constitutes the backbone and the foundation upon which to build an ecosystem of innovation, that helps SMEs to: (1) manage the distributed collective intelligence in the new product development; (2) design and develop modular products in a collaborative way. The action research methodology has been chosen for its appropriateness to the characteristic of this study.

Since the rise of the "open innovation" concept [START_REF] Chesbrough | Open Innovation: The New Imperative for Creating and Profiting from Technology[END_REF], much has been said about the benefits that companies may gain by opening up their innovation process. According to the open innovation paradigm, firms have the possibility to explore new sources for innovation, through an "inbound openness", with the aim of capturing technologies and ideas originated outside the organization's R&D facilities, and transfer them into the company. The underpinning concepts of the open innovation paradigm, which can be summarized in the exchange of ideas, interaction with external partners and new forms of collaboration among the actors involved, may be contextualized in another paradigm, that emerged in the digital domain in the same years of the spreading of the open innovation: the rise of Web 2.0.

With the "new" web applications able to facilitate collaboration, knowledge sharing and engagement among the users, the Web 2.0 has immediately represented an improvement in respect to the Web 1.0, characterized by a static use by the users without the possibility of interaction, and an opportunity "to harness collective intelligence" [START_REF] O'reilly | Web 2.0 Compact Definition: Trying Again[END_REF].

It appears clear how the web has straightway signified for firms a valid enabler to the open innovation practices. In the literature, we can find relevant contributions that depict the prominent role of the web as an intermediary for innovation: Sawhney et al. illustrate the communities of creation [START_REF] Sawhney | Collaborating to create: The Internet as a platform for customer engagement in product innovation[END_REF], and in particular the role of Internet as a platform for a valuable collaborative co-creation with customers. Pisano and Verganti propose four way to collaborate in the context of open innovation [START_REF] Pisano | Which kind of collaboration is right for you[END_REF], two of which are made possible by the presence the web. Boudreau and Lakhani propose two ways to organize external innovators [START_REF] Boudreau | How to Manage Outside Innovation[END_REF]: collaborative communities vs. competitive markets, putting in evidence how the former are driven by cooperative relationships, while the latter tend to have competitive relationships among one other.

In this context, virtually no company should innovate in an individualistic and independent way: "the new leaders in innovation will be those who figure out the best way to leverage a network of outsiders" [START_REF] Pisano | Which kind of collaboration is right for you[END_REF].

Criticalities for SMEs in the collaborative innovation environment

Specifically, the present research is focused on a significant gap that regards the context of Small and Medium Enterprises (SMEs): while Internet technologies are applied more widely by the large organizations to facilitate collaboration in the trading activities, in the learning, studying and managing of business processes, or in the services providing, there are significant barriers to the diffusion of e-business and advanced web applications in the context of SMEs. Companies, especially SMEs, that want to adopt a collaborative approach have not yet developed innovative application tools and well-established methodologies. More specifically, some of the SMEs key needs in the field of collaborative innovation may be summarized as follows:

• have a closer proximity and accessibility to cognitive diversity, and have the ability to connect directly with other industries or with the final users, in order to collaborate on innovation practices; • be guided to realize the possibilities to access knowledge, for learning how to build an own innovation and social capital: "the collective capacity of a firm to innovate"; • be helped to identify future emerging trends and technological-economic discontinuities, structuring a network approach with others, that permits to capture the "weak signals"; • be supported in the managing of technical and creative approaches during the developing of ideas for new products and services; • be helped to reduce market risks associated with investments in innovation and R&D, through the development of outside networks of intelligence which result in feedback from users and from the market; • be driven to improve the design and product development process, in order to receive suggestions and co-develop a product or a service.

In particular, focusing on the latter aspect, there is limited research on how web communities can positively impact the product development process of SMEs. One interesting study in such context is offered by Pritchard et al. [START_REF] Pritchard | Commercial Partnership for Collaborative Product Development (CPD)[END_REF], who describe the effort of a group of Singapore SMEs to cooperate and set up a partnership to carry out product development in a design chain, through a set of tools which assists SMEs to quickly and profitably bid for and carry out early development of a major sub-section of a new product. This project is focused on collaboration during the early phases of product development, and people augmentation, not replacement, by enhancing natural communication and creativity. Therefore, our research is directly linked to these recent areas of study, and aims to deal with them through an operative and functional approach in order to match the needs of SMEs.

The product development process in the context of collaborative innovation

The product development process lacks of investigation in the aspects related to its integration in a web-based collaborative environment [START_REF] Pappas | Development of a web-based collaboration platform for manufacturing product and process design evaluation using virtual reality techniques[END_REF] especially when considering the context of SMEs. Thanks to a literature review and a mapping of the existing solutions, we were able to shape a comprehensive "state of the art" of portals and tools, as described in Table 1.

Portals allow communities to interact through basic design environments. They provide different services, often in a crowdsourcing modality. Existing examples are several and heterogeneous, that we grouped into three main categories:

• Innovation marketplaces support companies in their R&D problems through innovation challenges. Even though the innovation seekers may be any type of companies, these challenges are mostly powered by large organizations, in spite of SMEs. The support given by these portals is limited to the initial phase of the R&D and innovation process, when seekers need to find proper solvers, but platforms usually do not sustain companies in the product development process. • Creativity hubs, as the innovation marketplaces, use contests to match seeker and solvers, but in this case to support creativity instead of innovation. Their aim is to create ad-hoc and spot collaboration between organizations and creative designers. As before, they do not to sustain the entire product development, but the focus is instead on the resolution of specific design problems. Considering the main gaps of the analyzed tools, they are mainly developed with an internal focus to the firm, in other words they typically allow only the interaction between designers of the same company, which are located in different production sites, through a web-based client-server. Moreover, the main solutions integrate tools, such as Computer Aided Design and Manufacturing (CAD/CAM) and Virtual Reality, or key elements of a single stage of the product development process, focusing mainly on the prototyping phases, which are generally located at the end of the product development process [START_REF] Cooper | Stage-gate systems: A new tool for managing new products[END_REF]: the development of tools that facilitate collaborative interaction of various technical, planning and managerial skills and competences in product development must also take into account the upstream stages of the process, namely the concept generation, the functional analysis and the product improvement, allowing to analyze aspects related to cost reduction, functionalities improvement, thus enhancing the overall quality, safety and sustainability of the product.

Furthermore, a comprehensive tool requires an "external" perspective, in which not only internal designers, but also other skills may be involved in the process, embracing the paradigm of "open innovation" and following the evolution of the traditional web towards the perspective of Web 2.0.

In this context, companies should leverage on some key elements such as trust users as co-developers intended to link the various sources to connect [START_REF] O'reilly | Web 2.0 Compact Definition: Trying Again[END_REF].

Therefore, the evolution of the product development process in a web-based application for the development of Collective Intelligence must take account of the opportunity to integrate seamlessly some established methodologies used in the traditional product development process, although they have not been explored for possible implementation within a web platform.

Research design

In order to address these research gaps, our research aims at investigating how is it possible to design and develop a web-based platform aimed at supporting SMEs in the product development process. The objectives of our study are twofold: to select the tools and methodologies that can effectively contribute to product development in a web-based environment for collective innovation on the basis of practical user needs within SMEs, and to adapt them seamlessly within a ICT platform.

The action research methodology [START_REF] Coughlan | Action research for operations management[END_REF] has been chosen for its appropriateness to the characteristic of this study, since the main research objective is investigating the development and the implementation of a collaborative web platform aimed at supporting SMEs in the product development process. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7-SME 2008-2) under grant agreement no. 243593, project COLLECTIVE -"Emerging communities for collective innovation: ICT operational tool and supporting methodologies for SME Associations".

Action research may be defined as an emergent inquiry process to solve real organizational problems, simultaneously bringing about change in organizations and adding to scientific knowledge [START_REF] Shani | Organisation inquiry: Towards a new model of the action research process[END_REF]. In fact, this methodology entails the active participation of researchers in a change process [START_REF] Coughlan | Action research for operations management[END_REF]. For this reason action research is appropriate when studying the development of a collaborative web platform, as actually happened in our study.

As usually happens in action research, the platform we developed emerged from iterative cycles of data gathering, feedback, analysis, action planning, implementation and evaluation in close interaction with SMEs and other research institutions involved in the research project.

Platform development

The developed ICT platform, called iCommunity, which constitutes the backbone and the foundation upon which to build an ecosystem of innovation, especially tailored to fit SMEs needs, allows to:

• manage the distributed collective intelligence and creativity in the new product development process in order to find new product ideas and its business model; • design and develop modular products in a collaborative way, reducing the market risk from the time when significant input and feedback from customers and users become an integrated component with the overall process design.

The focus of the research was also to include selected design methodologies (e.g. FMEA, FAST diagrams, Value Analysis, QFD, etc.) on the basis of practical SMEs user needs, and to adapt them seamlessly within the iCommunity platform. Based on the needed functionalities within each of the product development phases, supporting design methodologies were selected and included in the form of desktop or web-based tools to enable complex design performance. This was done by adding methodologies related tools to a specific functionality within selected design phase. The aim was to assist future users of the platform and propose a specific methodology when dealing with specific design domain functionalities in the platform. In this manner, the platform supports the users during the whole design process, thanks to the integration of methodologies and tools, thus filling the gaps detected in our mapping (Table 1). Design methodologies may add complexity to the design process, especially when considering SMEs that could not have developed structured design and product development competences: in fact, during the action research process, less than five per cent of the SMEs involved in the research project answered to our questionnaires that they are familiar with any of the design methodologies. Therefore, it was crucial to include less complex tools or partial tools that do not support a specific methodology as a whole but only answer problems related to specific functionality of the design domain.

Moreover, tutorials for implementing methodologies and tools are available within the platform: users can choose interactive video based lectures (e.g. PPT slides) and PDF descriptions. To support SMEs users, an overall guidance is also provided to explain the navigation across the whole design workflow.

Users can choose the methodologies, tools and related tutorials as "blocks", in order to structure the workflow across the whole design process, modeled on Cooper's Stage-Gate model [START_REF] Cooper | Stage-gate systems: A new tool for managing new products[END_REF], as represented in Figure 1: among the main platform functionalities, when entering a new product development/product improvement project, users can configure the workflow on the basis of their needs.

The project initiator can also choose collaborators from the platform users' network to involve them in the project and review activities after each design stage. Thanks to a semantic engine, the platform also suggests to the users potential workflows, collaborators and resources, on the basis of the information given at the beginning of the design process in the inception phase.

Fig. 1. Design process workflow

Moreover, data and information created in the whole design process can be stored and retrieved in the platform knowledge repository for future projects to allow knowledge transfer and reuse across different projects, which has been highlighted as a common pitfall in product development research [START_REF] Formentini | Using value analysis to support knowledge transfer in the multi-project setting[END_REF].

Conclusions

From an academic perspective, our research contributes to the product development research stream with innovative insights in the context of collaborative innovation and web-based platforms, by offering:

• A classification of the "state of the art" of portals and tools that can support the product development process; • A critical analysis of the methodologies and tools identified on the basis of SMEs target-users practical needs and of the identified trade-offs (i.e. usability, cost, time) for their subsequent introduction into the collective innovation platform iCommunity; • The modeling and adaptation of the tools chosen for their inclusion in the design workflow within the iCommunity platform, in order to fill the previously described research gaps.

Focusing on managerial implications, the development of the iCommunity platform represents an important opportunity for SMEs to effectively support their product development process. Future developments of the study will be focused on the complete validation of the platform in order to measure performance improvements in the product development process of the involved SMEs with a longitudinal perspective: the vision for the future is that the iCommunity platform will become a forum of research and development for innovation. The citizens of this agorà, fueled in particular by universities, software developers, experts, consultants, are associations and SMEs and their staff, but also visitors to the web, the "external" world.

•

  Distributed factories are the place were advanced manufacturing technologies, such as 3D printers, match with the Web 2.0; they offer a world-wide prototyping and manufacturing service integrated with on-line CAD-CAM tools.More than 50 standalone design tools were identified and analyzed. These tools range from simple open source web-based tools to commercially available desktop-based tools. The most popular tools regard established design and product development techniques such as QFD, FMEA and TRIZ methodologies. These tools help firms to manage specific issues throughout the phases of the product development project.

Table 1 .

 1 Mapping of portals and tools

	Typology	Category	Examples	Gaps
		"Innovation marketplaces"	Innocentive Hypios NineSigma Idea Connection Innovation Exchange	Challenges are mostly powered by large organizations, in spite of SMEs. Support limited to the initial phase of the R&D and innovation process.
			Crowdspring
	Portals	"Creativity hubs"	Idea Bounty 99Design Choosa Zooppa BootB Threadless Quirky Logo Design Team	Ad-hoc and spot collaboration between organizations and creative designers. The product development process is not entirely supported, since the focus is on specific design problems.
			wooshii
		"Distributed factories"	Ponoko eMachine Shop Shapeways	Focus on the last part of the product development process (i.e. prototyping and manufacturing phases).
				Lack of connection with
			QFD Builder	other tools or portals
		"Online tools"	Gliffy's SWOT Analysis	Need of tutorials to support new users (especially SMEs)
			Sunglass.io	in the methodology
	Standalone			implementation
	tools		FMEA	Lack of connection with
		"Desktop-based tools"	Facilitator Autodesk Design Review	other tools or portals Risk of creating "isolated" documents
			Acclaro DFSS	Limited interaction


Dario Antonelli

Giulia Bruno

Antonia Schwichtenberg

Agostino Villa

Full exploitation of Product Lifecycle Management by integrating static and dynamic viewpoints

Keywords: PLM modeling, information integration, ontology, UML, IDEF0

Even if PLM offers a wide range of functionalities, they are currently not fully exploited by most of the companies, which use it mainly as a file manager. In this paper we aim at helping the full exploitation of PLM systems. To this aim, we propose a model of the product lifecycle management in the form of an ontology integrating both the static structure of product's data and the dynamic description of the related processes.

Introduction

Initially, to control data proliferation in design activities, Product Data Management (PDM) systems were created, which allowed data integration and secured files exchange. Within PDM the focus was on managing and tracking the creation, updating and storage of all information related to a product (e.g., CAD models, FE simulations and administrative documents). Product Lifecycle Management (PLM) is the logical evolution of PDM systems. PLM integrates people, data, software, processes and business systems to provide a complete product information backbone [START_REF] Stark | Product lifecycle management: 21st century paradigm for product realization[END_REF]. However, the concept of PLM is not completely received by companies, which do not exploit the full potentiality of PLM systems but use them only for data management functionalities. Especially for small and medium enterprise the exploitation of PLM is a problem due both to the lack of business process models and strict roles separation.

The paper provides an approach to allow a better exploitation of PLM systems by integrating static and dynamic viewpoints of product lifecycle. Particularly, we propose a model to contemporarily represent the data structure of PLM, thus allowing interoperability and data exchange among different systems, and the dynamic processes of product lifecycle. The model is presented in the form of an ontology, due to the ontology capability of including both the static representation (in its structure) and the dynamic view (in its instances).

Ontologies support several useful features, such as to share common understanding of the structure of information among human or software agents, to enable reuse of domain knowledge, to separate domain knowledge from operational knowledge and to provide formal analysis of terms [START_REF] Matsokis | An ontology-based approach for Product Lifecycle Management[END_REF]. Ontologies have already been proposed for knowledge management in product design processes [START_REF] Brandt | An ontology-based approach to knowledge management in design processes[END_REF]. Ontologies have been devel-oped for closed-loop PLM [START_REF] Jun | A primitive ontology model for product lifecycle meta data in the closed-loop PLM. Enterprise Interoperability II: New Challenges and Approaches[END_REF], for the Open Assembly Model [START_REF] Fiorentini | Towards an Ontology for Open Assembly model[END_REF], and for STEP (Standard for the Exchange of Product Model) [START_REF] Wang | Ontology based geometry recognition for STEP[END_REF]. A product design ontology that formalizes the functionality of shape processing methods in the design workflow is defined in [START_REF] Catalano | A product design ontology for enhancing shape processing in design workflows[END_REF], while an ontology to manage both the product and the PLM is proposed in [START_REF] Matsokis | An ontology-based approach for Product Lifecycle Management[END_REF]. A method for semantic integration of PLM objects based on an integrated ontology is described in [START_REF] Kwak | An Approach to Ontology-Based Semantic Integration for PLM Object[END_REF] and the work in [START_REF] Suh | UbiDM: a new paradigm for product design and manufacturing via ubiquitous computing technology[END_REF] uses ontologies for interoperability and present a model for using data of the entire life of the products as an input for the design and production of new products. Differently from the previous works, in which the ontologies were developed starting from general concepts of the product processes or from general models of PLM, we generated an ontology from the integration of the static structure of PLM and the dynamic description of the product lifecycle processes.

The rest of the paper is organized as follows. Section 2 describes the dynamic model of product lifecycle, while Section 3 provides the static structure of PLM. In Section 4 the derived ontology is presented. Finally, Section 5 draws conclusions and states future works.

Dynamic model of product lifecycle

The dynamic (or behavioral) model makes explicit the processes and the interactions among entities involved in the product lifecycle. IDEF0 [START_REF] Hu | The Development of The Lifecycle Function Model By IDEF0 For Construction Projects[END_REF] is selected in the present study as a formalism for the dynamic representation due to two reasons: (i) it is a well-established methodology in the industrial environment, thus companies had less problems representing their processes with this formalism, and (ii) it is suitable to our scope because it allows the separate identification of resources, files and tools inside activities. Furthermore it was demonstrated that IDEF0 models can effectively complement UML models when the objective is the system design instead of the software development [START_REF] Kima | The complementary use of IDEF and UML modelling approaches[END_REF].

Product lifecycle is a complex process and it is composed by several phases. We focused on the Beginning of Product Life (BPL), trying to generalize the very detailed process of the car body and chassis design employed by an automotive industry. Since this process is among the most long and complex, it represents a good starting point to build a general model. BPL has been divided in Concept Design, Preliminary Design, Detailed Design, and Development. For sake of shortness we report in Fig. 1 the overall activity diagram (A0) and in Fig. 2 the details of the first activity, i.e., the Concept Design (A1). The concept design is composed of activities oriented to the definition of the schematic design of product and process and to the set-up of general project guidelines. Care should be paid to the fact that the position of blocks in the diagram does not represent the temporal sequence of activities. As an example, in the investigated companies the economic feasibility study follows the engineering feasibility study (see Fig. 2). The static (or structural) model aims at representing the structure of the system by using entities, attributes, relationships and operations. The most used formalism for structural models is the Unified Modeling Language (UML) class diagram [START_REF] Fowler | UML distilled[END_REF], which has been already used to model PDM systems [START_REF] Eynard | UML based specifications of PDM product structure and workflow[END_REF].

The UML class diagram of PLM is reported in Fig. 3. The upper part of the diagram contains information about the product and its characteristics, while the most consistent part is focused on the product lifecycle management.

Fig. 3. UML class diagram of PLM

Since we are not interested in providing a detailed description of the product (for a more complete UML diagram of a product, the Core Product Model developed in [START_REF] Foufou | A Core Product Model for PLM with an illustrative XML implementation[END_REF] can be taken as a reference), we modeled the most important concepts of product in three classes. The class Product contains the information about products. It is associated to the Product_Group class, which contains information about the product group it belongs to. The composition association between the Product class and the Prod-uct_Component class represents the fact that each product can be made of several components, and each of them can have associated files that describe their state in the PLM.

The Product class is associated with the Life_Cycle_Phase class. The association between Life_Cycle_Phase and Activity means that each phase contains several activities. There are several kind of association between Activity and File, due to the different kind of usage of the file done by the activity. An activity can (i) consult a file, if it is simply read, (ii) have a file as input, if it is used or modified by the activity, and (iii) produce a new file as output. A file can be also connected to the product or product component it refers to. Furthermore, files or groups of files can be linked in a Document (e.g. a report). For each file is known the Tool exploited to produce it. To keep trace of the persons involved in the activities, the Personnel_Resources class was introduced, which include personal data, contacts, etc. For each person is specified the assigned task for each activity, to keep trace of the collaborative relationships among resources. Furthermore, to each person is assigned a Role, which specifies if a person can see an Activity, can use a Tool or can access to a File. In the last case, the access right can be specified as reading or writing by means of the associative class Access_Rights. Finally, in addition to the activities related to the product management, we also modeled the Management_Event class to considered all the events done to control the state of an activity (i.e., final approval of results, etc.).

PLM ontology

An ontology formally represents knowledge as a set of concepts, properties and relationships within a domain. Thus, it can be seen as a structural model. However, it is also possible to instantiate an ontology, i.e., assign real values to class properties and relationships. Thus, the ontology also includes information deriving from a behavioral model. Furthermore, an ontology offer additional functionalities, such as the possibility of making reasoning and logical inference among data. For these reasons we decided to resort to an ontology to provide a way to fully model a PLM system.

The ontology is formalized in ObjectLogic [9], a successor of F-logic [START_REF] Kifer | Logical foundations of objectoriented and frame-based languages[END_REF]. This gives us the opportunity to add rules, to integrate easily other data sources and to query the data efficiently. The ontology model can also be exported to OWL without any lost. The ontology was developed with the OntoStudio software [10] and is reported in Fig. 4.

The schema of the ontology is a direct representation of the structural model for PLM described in the UML diagram. The classes in the UML are concepts in the ontology with named relations between them having the cardinality restrictions and the names as defined in the UML. For the attributes, it is possible to define ranges according to the XML Schema Definition [11]. So our model can be used together with any repository offering URIs for the documents stored there (e.g. by providing a REST interface).

Once the model has been developed, it is possible to fit it with the data providing from the behavioral models, i.e., to give real values to products, activities, files, resources, etc.. This forms the workflow within a concrete product lifecycle. Fig. 5 shows an example of an instantiation of our ontology for a Production Planning activity of a small industry.

Fig. 4. Ontology of PLM

We would like to clarify that the proposed ontology aims at helping companies that would like to adopt a PLM system, and thus need to first produce a behavioral model of their process, and then to organize the information about product lifecycle among the entities and relations specified by the static UML class diagram of PLM. To have a more complete ontology of the system, additional information arising from the specific PLM software chosen by the company should be also considered.

Conclusions

In this paper we describe an approach to full exploit PLM systems by integrating static and dynamic viewpoints. Particularly, we propose a model of PLM in the form of an ontology, due to the ontology capability of including both the static view (in its structure) and the dynamic view (in its instances). We are currently working on applying our approach to some industrial cases to show how this general model can improve the product lifecycle management.

Fig. 1 .Fig. 2 .

 12 Fig. 1. IDEF0 diagram of product Beginning of Life

Fig. 5 .

 5 Fig. 5. Example of an instantiation of the PLM ontology for a Production Planning activity

Acknowledgments

The research presented in this paper is supported by the EU-FP7 research project on Advanced Platform for Manufacturing Engineering and Product Lifecycle Management (amePLM).


Fatih Karakoyun
email: fatih.karakoyun@epfl.ch

Dimitris Kiritsis
email: dimitris.kiritsis@epfl.ch

Closed-Loop Life Cycle Management Concept for Lightweight Solutions

Keywords: Wrought aluminum alloys, Closed-Loop PLM, LCA, LCC

Lightweighting is the point of interest especially for automotive and aerospace industries. Wrought aluminum alloys have great potential in lightweighting. There is a need to increase post-consumer recycling and use of recycled aluminum in high-end structural components to exploit the full potential of wrought aluminum alloys. Closed-loop product life cycle management (PLM) may enable to increase the recycling of wrought aluminum alloys by providing essential information about the individual parts. Considering separate life cycle phases, products and processes may seem environmentally friendly, but it is not possible to be sure unless the entire life cycle is taken into account. LCA and LCC are scientific investigative processes which take into account entire life cycle of the products and help decision makers to evaluate between alternatives in product development. The work

Introduction

Lightweighting is essential for automotive and aerospace industries for a number of reasons. First of all lightweighting directly reduces the fuel consumption because the energy required to move a vehicle is, except for aerodynamic resistance, directly proportional to its mass [1]. Due to the reduction of the fuel consumption GHG emissions are also reduced. There are metal (high strength steel, aluminum, magnesium) and composite (SMC, glass fiber and carbon fiber) alternatives for lightweighting. Among all other alternatives aluminum is the most feasible choice in both environmental and economic sense. Aluminum, especially wrought aluminum alloys, have large potentials for dramatic weight reduction of structural parts. Recycling is a major aspect of continued aluminum use, as more than a third of all the aluminum currently produced globally originates from old, traded and new scrap [2].

Global recycling rate of aluminum in transport is ~90% [START_REF] Gesing | Recycling Light Metals from End-of-Life Vehicles[END_REF], which is driven by the high material value of aluminum scrap. However the recycling rate of aluminum in transport is high, most of the aluminum scrap is recycled into cast products due to number of reasons. One of the most important reasons is lack of sufficient information about the parts produced from wrought aluminum alloys. It is not possible to track and gather information about individual parts after delivery of the vehicle to the customer. The closed-loop product life cycle management (Closed-loop PLM) system focuses on tracking and managing the information of whole product life cycle, with possible feedback of information to product life cycle phases [START_REF] Jun | System Architecture for Closed-loop PLM[END_REF]. Closed-loop PLM may enable to increase the recycling rate of wrought aluminum alloys by providing essential information about the individual parts. The work reported in this paper is a funded by EU FP7 project called SuPLight, which aims to increase use of recycled material in production of high-end structural components.

Background

Weight savings in the overall car mass is considered to be a major research focus [START_REF] Ungureanu | Life-cycle Cost Analysis: Aluminum versus Steel in Passenger Cars[END_REF].

Weight reduction is particularly important because average vehicle weight is expected to increase since the automobile industry will continue to market new models with increased luxury, convenience, performance, and safety as demanded by their customers [START_REF]Improving Sustainability in the Transport Sector through Weight Reduction and the Application of Aluminum[END_REF]. Aluminum is proven to be among the potential materials capable of achieving weight reduction without sacrificing the vehicle safety and performance [START_REF] Ungureanu | Life-cycle Cost Analysis: Aluminum versus Steel in Passenger Cars[END_REF].

Wrought aluminum alloys have great potential in weight reduction. Tests have shown 75% reduction of the weight with sustained performance using wrought aluminum when compared to a conventional steel based solution, and 50% reduction when compared with aluminum castings [START_REF]Improving Sustainability in the Transport Sector through Weight Reduction and the Application of Aluminum[END_REF].

Recycling aluminum saves 95% of the energy and 95% of emissions associated with production of metal from the ore, as well as reduces the amount of waste consigned to landfill [START_REF] Green | Recyclable Aluminum Rolled Products[END_REF]. Green stated that on life cycle basis all the energy required to produce primary aluminum is recovered in vehicle fuel savings within 3 years, and in less than 3 months when recycled metal is used [START_REF] Green | Recyclable Aluminum Rolled Products[END_REF].

The European Union is structurally dependent on aluminum recycling for its domestic metal supply, because of limited ore mining and lack of sufficient domestic primary aluminum production, growing end-use demand and energy constraints in Europe [START_REF] Gesing | Recycling Light Metals from End-of-Life Vehicles[END_REF][START_REF] Boin | Melting Standardizing Aluminum Scrap: A Mass Balance Model for Europe[END_REF]. More than half of all the aluminum currently produced in the European Union (EU-27) originates from recycled raw materials and that trend is on the increase, however it should be noted that Europe is a net importer of aluminum and aluminum ore. In 2010, 79% of the bauxite, 41% of alumina for production of primary aluminum and 30% of total aluminum supply was net-imported by European aluminum industry. From a total metal supply of 12.5 million tons in 2010, 35 % is produced by European primary smelters, 30% is net-imported and 34% is recycled by European refiners and remelters [9]. Efficient aluminum recycling will be more important in the future because of the import dependence and rising energy constraints in Europe.

However recycling is the key issue to increase the use of aluminum, wrought aluminum recycling is difficult due to their low impurity and alloy content. Kevorkijan stated that the main difficulty in production of wrought aluminum alloys from scrap is to achieve the proper chemical composition of the melt with minimal addition of primary aluminum and alloying elements [START_REF] Kevorkijan | Advances In Recycling Of Wrought Aluminum Alloys For Added Value Maximization[END_REF]. Whether wrought alloys completely made from wrought alloy aluminum scrap can assume the same quality as the primary aluminum alloy is frequently questioned. Over time, it has become clear that such concerns are largely unwarranted; with very few exceptions, quality requirements can be met by remelted material [START_REF] Kevorkijan | The Recycle of Wrought Aluminum Alloys in Europe[END_REF].

Das studied on recycling aerospace alloys in which cost effective recycling is difficult because aircraft alloys are typically high in alloying elements and contain low levels of impurities to optimize toughness and other performance characteristics [START_REF] Das | Recycling Aluminum Aerospace Alloys[END_REF][START_REF] Das | Recycling Aluminum Aerospace Alloys[END_REF]. He emphasized that the most overlooked aspect of maximizing recycled metal appeared to be the development of new alloys tailored to meet composition and performance criteria when produced directly from recycled metal [START_REF] Das | Emerging Trends in Aluminum Recycling: Reasons and Responses[END_REF].

Scrap for the production of wrought alloys should be sorted with strict control of the concentration of alloying elements in order to achieve the prescribed compositional tolerances [START_REF] Kevorkijan | Advances In Recycling Of Wrought Aluminum Alloys For Added Value Maximization[END_REF]. The efficiency of collection and sorting of aluminum scrap could be maximized by accurate life cycle information of the product. Jun et al. have provided system architecture and framework for closed-loop PLM. Closed-loop PLM allows all actors of the whole lifecycle to access, manage, and control product related information, especially, the information after a product delivery to the customer and up to its final destiny, without temporal and spatial constraints [START_REF] Jun | Research issues on closed -loop PLM[END_REF]. In the closed-loop PLM, it is possible to know the product location and usage history and to predict the degradation status and remaining lifetime of parts or components. Recyclers/reusers may be able to obtain accurate information about value materials arriving via EOL routes by closing the product life cycle information loop [START_REF] Jun | System Architecture for Closed-loop PLM[END_REF]. Material recycling can be significantly improved because recyclers and re-users can obtain accurate information about ''value parts and materials'' arriving via EOL routes: what materials they contain, who manufactured them, and other knowledge that facilitates material reuse [START_REF] Jun | System Architecture for Closed-loop PLM[END_REF].

Closed loop PLM allows to gather information but it is also necessary to evaluate economic and environmental performance of the products and processes. Life cycle analysis (LCA) is one such tool that can help companies to understand the environmental impacts associated with their products, processes, and activities [START_REF]Note on Life Cycle Analysis[END_REF]. Life cycle costing is a methodology directed at the evaluation of all the costs associated with an activity or a product over its entire life cycle, thus assuming the dual role of a Life Cycle Assessment in economic terms [START_REF] Giudice | Life Cycle Cost Analysis[END_REF]. LCA and LCC help decision makers chose the most environmentally friendly or less costly alternative among alternatives.

Closed loop PLM

Capability of tracking their products and components is an important issue for car manufacturers and their suppliers, OEMs. They need to have seamless information flow about the location, situation and working conditions of their products through in all phases of their life cycle because they are strategically important, valuable, dangerous etc. In general, the product lifecycle consists of three main phases: beginning of life (BOL), including design and production; middle of life (MOL), including logistics (distribution), use, service, and maintenance; and end of life (EOL), including reverse logistics (collecting), remanufacturing (disassembly, refurbishment, reassembly, etc.), reuse, recycle, and disposal [START_REF] Jun | Research issues on closed -loop PLM[END_REF]. Holistic lifecycle approach, depicted in Fig. 1, takes into account material and data/information flows, and provides a broader perspective to all activities of the product or process through all phases of the lifecycle.

Fig. 1. Holistic life cycle approach

All of the car manufacturers and most of their suppliers use PLM systems to manage their business activities. Although the vision of the PLM is to gather information through the life cycle of the product and produce knowledge for all the actor of the life cycle, lack of efficient tools, in efficient use of existing tools makes it impossible to enable this. On the other hand, closed-loop PLM that is equipped with product identification and communication technologies may enable to meet the expectations from PLM. The closed-loop PLM system focuses on tracking and managing the information of the whole product lifecycle, with possible feedback of information to product lifecycle phases. It provides opportunities to reduce the inefficiency of lifecycle operations and gain competitiveness [START_REF] Kiritsis | Product lifecycle Management and Embedded Information Devices[END_REF].

Fig. 2. Framework for closed-loop PLM

Closed-loop PLM may enable to establish a close loop recycling framework for lightweight components. The technology needed for close loop recycling of lightweight components is simpler, economical and more environmentally friendly than, recycling the high end components to cast aluminum and produce the components from virgin material. One of the most important limitations for close loop recycling is the lack of sufficient information, and impossibility of tracking the products especially after end-of-life. The concept of closed-loop PLM, shown in Fig. 2, can be defined as follows: a strategic business approach for the effective management of product lifecycle activities by using product data/information/knowledge which can compensate PLM to realize product lifecycle optimization dynamically in closed loops with the support of PEIDs and product data and knowledge management (PDKM) system [START_REF] Jun | Closed-loop PLM. Advanced Manufacturing: An ICT and Systems Perspective[END_REF].

As most of the cars have on board computer in which life cycle information is recorded and stored, the manufacturers have the opportunity to follow the components of their choice. For OEMs it is more difficult to have sufficient information about their products. One of the most important reasons is that OEMs are not partners with the car manufacturers but suppliers for them. The manufacturers do not have any responsibility, need or urge to share information about the components. OEMs are given dimensional and mechanical requirements that their part should provide but they do not get any feedback unless there is a problem about their products. With the current trends and legislations like, extended product responsibility and End-of-Life vehicles directive, car manufacturers have the responsibility to take care of their products after the end-of-life. They should also share the responsibility with their OEMs in order to do this properly and economically.

In closed-loop PLM, all life cycle actors should ensure secure and accurate flow of information related with the material flow. To establish a closed loop recycling framework, information flow through the EOL phase should be ensured. EOL is the phase where EOL products are collected, disassembled, refurbished, recycled, reassembled, reused or disposed. Closed-loop PLM may make it possible for dismantlers and recyclers to obtain accurate information about the materials arriving to EOL phase. The dismantler may find out, which components are worth disassembling, the condition of the components to decide the best disposal option and who are interested in these components. The recyclers may find out, the amount and composition of the material they will receive.

Reverse Logistics

To establish a close loop recycling framework for the light weight components other than having an information system to track and manage the product life cycle information (closed-loop PLM framework) and it is also necessary to have a reverse logistics framework to enable the collection and transportation of the lightweight components back to their manufacturers. Reverse logistics is defined as; the process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of con-sumption to the point of origin for the purpose of recapturing value or proper disposal. More precisely, reverse logistics is the process of moving goods from their typical final destination for the purpose of capturing value, or proper disposal [START_REF] Rogers | Going Backwards: Reverse Logistics Trends and Practices[END_REF].

In the last decades, economic, legislative and social engaging incentives have driven industrial sectors and governments to become active in reverse logistics. The engagement of companies with society and environmental issues also can generate incentives to manage return flows in a supply chain. Moreover, marketing, competitiveness and strategic issues are other incentives for companies to become active in reverse logistics [START_REF] Cruz-Rivera | Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico[END_REF].

In case of lightweight components which are made of materials that are economically valuable and tailored to meet the requirements of the car manufacturers and assure their function, reverse logistics of the components. In practice, ~90%of an automobile is recycled; however precious lightweight components made of aluminum and magnesium need special afford to retrieve the effort spent for them. Otherwise high end automotive and aeronautic parts turn into door handles and coat hangers. With the help of closed-loop PLM, accurate information may be fed into reverse logistics framework and the EOL actors will be aware of the amount and the condition about the components. By the help of this information, the EOL actors may take action and arrange their business activities with their stakeholders.

LCA & LCC of Automotive Components

The footprint of a product is the environmental impacts caused directly and indirectly during the life cycle of a product from raw material acquisition through production use and disposal. LCA is an internationally recognized approach that evaluates the potential environmental and human health impact associated with products and services throughout their life cycle. Among other uses, LCA can identify opportunities to improve the environmental performance of products at various points in their life cycle, inform decision-making, and support marketing and communication efforts. The unique feature of LCA is the focus on products in a life-cycle perspective, which is useful in order to avoid problem shifting [START_REF] Finnveden | Recent developments in Life Cycle Assessment[END_REF]. Automotive industry supports holistic life cycle approach and promotes using LCA to evaluate the life cycle environmental impacts of new designs and technologies and the effect of decisions made through the life cycle of their products. Furthermore, it is also necessary to evaluate the economic performance of the components, which is more interesting for the manufacturers. Life cycle cost (LCC) analysis is a tool that produces important metrics for choosing the most cost-effective approach from a series of alternatives. LCC generally refers all the costs associated with a product throughout the product's life. [START_REF] Kleyner | Minimizing life cycle cost by managing product reliability via validation plan and warranty return cost[END_REF] A major portion of the projected life-cycle cost (LCC) for a specific product, system, or structure is traceable to decisions made during conceptual and preliminary design. These decisions pertain to operational requirements, performance and effectiveness factors, the design configuration, production methods and quantity, utilization factors, logistic support, phase-out planning, and disposal [START_REF] Fabrycky | Life-Cycle Cost and Economic Analysis[END_REF].

Case study: Front lower control arm (FLCA)

The FLCA is one of the case studies of SuPLight project. FLCA is used to connect suspension members to the vehicle's chassis and to control both the lateral and longitudinal location of the wheel. Lightweighting the control arm improve the ride quality and handling, additional to fuel consumption and emissions reduction. The analysis showed that the hot spot in the life cycle of the FLCA is end-of-life processes. Material flows based on different production routes and different amount of recycled material in production have been defined. These models are further used in formation of LCA&LCC models. The proposed holistic life cycle approach might enable evaluation of not only the performance of the product and processes product producer, but also the performance of processes of other related life cycle actors. Closed loop PLM system is necessary to combine the life cycle actors and enable necessary information flow between life cycle actors. LCA&LCC are the decision support systems needed in the concept and closed loop PLM is capable of obtaining and processing the huge amount of data needed for LCA&LCC and distribute the performance characteristics to related parties for self-evaluation. Establishment of a reverse logistics framework is also necessary, and it should also be evaluated in economic and environmental senses.

Conclusion

Lightweighting is an important issue for automotive industry in order to reduce fuel consumption and emissions of vehicles. Wrought aluminum alloys have high potential in weight reduction of structural components. However, a close-loop recycling framework for these alloys is necessary to retrieve their full potential. Closed-loop PLM and reverse logistics framework are the two important elements of close loop recycling framework. Although, aluminum is thought to be environmentally friendly, it is necessary to evaluate the environmental performance of these components. LCA is such a tool that enables the estimation of the total environmental impacts resulting from all stages in the product life cycle. Other than environmental performance, economic performance of the lightweight components is important, that could be evaluated by LCC.

Acknowledgements. The authors would like to express our deep gratitude to all SuPLight partners and European Comission.


Eva Jerhotova
email: eva.jerhotova@honeywell.com

Marek Sikora
email: marek.sikora@honeywell.com

Petr Stluka
email: petr.stluka@honeywell.com

Dynamic Alarm Management in Next Generation Process Control Systems

Keywords: next generation DCS/SCADA, system of systems, process monitoring and control, alarm management, dynamic alarming, state-based alarming, alarm load shedding, software-oriented architecture, SOA, complex event processing, CEP, IMC-AESOP project

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

Introduction

Current industrial process control and monitoring systems -DCS/SCADAare becoming increasingly complex and heterogeneous [START_REF] Karnouskos | Architecting the next generation of service-based SCADA/DCS system of systems[END_REF]. Communication among their components is evolving from synchronous to asynchronous. In the system architecture design, multiple key challenges need to be appropriately addressed, such as interoperability, real-time performance, security or availability. The European R&D-project IMC-AESOP (ArchitecturE for Service-Oriented Process -Monitoring and Control, http://www.imc-aesop.eu) proposes consistent use of Service-Oriented Architecture (SOA). This architectural approach assumes that all autonomous and intelligent de-vices, applications and systems that are deployed nowadays or in near future will expose their capabilities, functions and structural characteristics as services. Under such paradigm, the industrial process environment will be mapped into a "Cloud of Services" where devices and applications distributed across different layers of the enterprise-control system hierarchy expose the services that they provide and at the same time they are able to access and use other services located in the cloud. The SOA approach [START_REF] Erl | Principles of service design[END_REF] has been widely adopted by enterprise architects for a number of reasons, which include increased flexibility in application design, providing opportunity of functionality reuse at the macro level (or the service level) rather than at the micro level (or the class level), and making the relevant businesses more agile and capable to respond more quickly to changing market conditions. The IMC-AESOP project aims at bringing the SOA architectural approach into the industrial automation world. A considerable part of the project efforts has been invested into describing the service-based architecture of a DCS/SCADA system and identification of services which could be exploited in multiple environments or applications. Some of the services are rather specialized, such as configuration services, data acquisition services, or alarm processing services, while other services may be more generic, such as discovery (e.g. service registry) or event broker services.

In the recent years, Complex Event Processing (CEP) [START_REF] Karnouskos | A SOA-based architecture for empowering future collaborative cloud-based industrial automation[END_REF] has gained considerable importance as a means to extract information from distributed event-based (or message-based) systems. It became popular in the domain of business process management but is now being applied in the industrial monitoring and control domains. This method enables efficient asynchronous communication (both within and across architecture layers) and a temporal reasoning functionality capable of processing large amounts of system-generated events in real time. CEP allows control systems to evolve from scan-based systems to event-based systems, which significantly increases the system performance and data throughput. In the IMC-AESOP project, Microsoft StreamInsight is used as a CEP engine. This engine is wrapped in an alarm processing service, which is being reused in different applications or use cases.

The remainder of the paper is organized as follows. Section 2 describes the advanced alarm handling techniques, whose specific implementations using SOA and CEP are being explored in the project. Section 3 summarizes the underlying architectural principles. And finally, section 4 discusses implementation challenges and plans for validation.

Advanced Alarm Handling Techniques

Alarms used in industrial manufacturing facilities are sound and/or visual announcements of abnormal process situations addressed to operators. These announcements are typically triggered when a given process variable (measured by a dedicated sensor) exceeds a predefined limit. Alarms require operator assessment and action in order to avoid (or at least mitigate) plant upsets, which may otherwise cause injuries, loss of production or equipment damage [START_REF] Rothenberg | Alarm Management for Process Control, A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems[END_REF][START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF][START_REF] Errington | Effective Alarm Management Practices[END_REF].

The alarming capability is ensured by the alarm system within the plant control system. As a consequence of process or equipment changes over time (e.g. equipment addition or replacement) the alarm system requires maintenance to sustain its best performance and full functionality. Alarm rationalization and continuous alarm system improvement [START_REF] Rothenberg | Alarm Management for Process Control, A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems[END_REF][START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF][START_REF] Errington | Effective Alarm Management Practices[END_REF][START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF] are the processes for achieving the goal of keeping the alarm system performance at an appropriate performance level during its life-cycle. During alarm system maintenance, certain alarms may be configured or de-configured and alarm parameters, such as limits, priority, deadband, or delay timer, may be changed.

Despite considerable investments into alarm system improvement efforts which have been made by operating companies, the performance of the alarm system in plant upsets remains a challenge and operators still experience alarm floods1 . Occurrence of alarm floods can be mitigated by the use of advanced alarm handling techniques [START_REF] Rothenberg | Alarm Management for Process Control, A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems[END_REF][START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF][START_REF] Errington | Effective Alarm Management Practices[END_REF][START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF], such as alarm grouping (i.e. presenting related alarms as a group), state-based alarming (i.e. using different alarm settings or suppressing alarms for different process or equipment states). This technique is sometimes called modebased alarming, dynamic alarm masking, alarm suppression, or automatic alarm shelving or alarm load shedding (i.e. displaying only the most critical alarms during alarm floods and delaying presentation of alarms of less critical).

The phenomenon of alarm flooding is one of the pain points in the process industry and it was one of the main driving forces for establishing dedicated professional consortia, such as the Abnormal Situation Management Consortium (ASMC). Within the IMC-AESOP project, the capabilities of SOA and CEP are demonstrated on the statebased alarming and alarm load shedding methods that have a great potential for alarm flood reduction.

State-Based Alarming

In certain process states, static alarms can be inadvertently triggered due to normal process changes (e.g. different operating mode or equipment shutdown). In such situations, certain alarms become meaningless or their limits must be set too wide to accommodate the different states. State-based alarming is a dynamic alarm handling method based on switching the alarm system configuration to the settings which correspond to the identified process states. For the different states, new alarms may be enabled, certain alarms may be disabled or their parameters may be altered (such as the alarm priority or the alarm limit).

The process states may be grouped into the following categories: Unit or equipment shutdown, such as stopping or shutdown of a redundant piece of equipment (e.g. when a redundant pump is turned off, all pump low-flow alarms are disabled), shutdown of a unit (e.g. for maintenance), or equipment out-ofservice. Fig. 1 displays an example of alarm settings for shutdown of a tank. For the shutdown state, the low-energy alarms (e.g. low level in the tank) should be deconfigured, while the high-energy alarms (e.g. high pressure) should not be deconfigured, but only reconfigured to much lower values in order to be able to detect isolation failures and unexpected reactions from contamination [START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF].

Different operation modes, such as start-up, steady operation, different feed, decoking (e.g. higher temperatures for heater decoking).

Major event suppression [START_REF] Errington | Effective Alarm Management Practices[END_REF], such as suppression of a suction pressure alarm when the compressor trips off. State-based alarming is becoming increasingly used in practice. This is true mainly for the shutdown-state alarming, for which the setup is relatively easy and many implementations already exist. In contrast, the other two groups of techniques are not currently much used because of requiring deeper analysis, such as the cause and effect analysis for consequential alarm suppression. Regarding state-based alarming implementation, switching between states may be done manually (by the operator), semi-automatically (when the operator either identi-fies or confirms the automatically identified state and initiates the configuration change), or automatically (when no input is required from the operator). For the automatic approaches, the state detection logic must be reliable and must not chatter. As depicted in Error! Reference source not found., the state-based alarming implementation must include reliable alarm settings enforcement and a fail-safe mechanism. Under any circumstances, the operator must be kept informed about which alarms have been disabled or reconfigured and why and also should be able to override the state change.

Alarm Load Shedding

Alarm load shedding is a technique which supports operators in prioritizing actions in alarm flooding situations by displaying the most urgent alarms, postponing displaying of less important ones, and filtering out alarms of log priority and alerts. The aim of this method is to keep the alarm rate at a manageable level (ideally one alarm per minute [START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF]) as applicable.

There are two options for triggering this method: manual (by the operator who may select a preconfigured filter) or automatic (based on alarm flood detection). The former approach already occurs in the current practice, while the latter is not yet used.

All alarms should be available at operator's request (e.g. in the graphical display) and no delay must never be applied to emergency and critical alarms for safety reasons [START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF]. To yield good results, this method requires a rationalized alarm system with appropriate alarm limits and priority ranking, elimination of chattering alarms, eclipsing, or advanced alarm handling techniques, such as state-based alarming [START_REF] Rothenberg | Alarm Management for Process Control, A Best-Practice Guide for Design, Implementation, and Use of Industrial Alarm Systems[END_REF][START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF][START_REF] Errington | Effective Alarm Management Practices[END_REF][START_REF] Andow | Alarm Flood Analysis Report[END_REF][START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF].

Advanced Alarm Management Using SOA and CEP

Alarm management functionality of a DCS/SCADA system is usually distributed among several system components including: Automation devices (or controllers) generating alarm events via systematic comparison of the actual values of all monitored points with the pre-configured alarm trip points (limits) and an alarm event is activated whenever specific trip point is reached A DCS server (connected to the supervisory control network) providing capability for automated logging (journaling) of alarm events.

A DCS station (connected to the same network) through which the process operator interacts with the control system. The DCS station provides graphical displays visualizing a chronological list of active alarms (i.e. alarm summary), process schematics, and other types of displays supporting operator situation awareness.

An application node representing a computer dedicated to alarm configuration, analysis and maintenance applications, which are usually designed for off-line use during alarm system improvement activities

The advanced alarm management techniques (described in section 2) are usually residing in the supervisory control network with individual functions shared between the server and stations. Several functional blocks (alarm configuration, alarm processing, event detection and others) are needed for successful implementation. Within the IMC-AESOP project, the advanced alarm management functions are realized through specific services or their combinations, as illustrated in Fig. 3.

The Sensory Data Acquisition service delivers data from the process exploiting either the scan-based method (sending values regularly) or the event-based method (delivering values only when there has been any change)

The System Configuration service provides information of the current process state (such as startup, normal operation, shutdown, maintenance, fault, off)

The Alarm Configuration service lists all available alarms with their description, properties, and relation to process units and equipment

Fig. 3. Alarm processing architecture

There are also several instances of the CEP service that are being directly used by the alarm processing services. Each of them has the same StreamInsight service running in the background. The difference lies in the input and/or output events and the definition of the query (as described below). The following two services are the core of the SOA/CEP-based alarm processing application:

Referring to Fig. 3, the State Estimation service uses the CEP#1 service, which evaluates predefined rules on the values of relevant process variables. For example, when a certain set of variables turns zero, it may be assumed that the associated unit has been turned off, or variables oscillation outside the predefined bounds may indicate that a corresponding unit is in a startup mode. The information about the identified state change of the unit is then stored.

The Alarm Processing service represents an implementation of the alarm handling techniques described above. The CEP#2 service generates alarm events via comparing given process variables with their limits, while taking into account the process state identified by the State Estimation service. The CEP #3 service reduces alarm chatter by concatenating multiple closely-spaced alarms into a single alarm event. Finally, the CEP#4 service provides the alarm load shedding functionality. As illustrated in Fig. 3, the alarms identified by the CEP#2 block can be stored in the alarm log for possible further investigation, while the operator is presented only with the alarms output by the CEP#4 block. The SOA nature of this application also allows dynamic combining of the individual CEP alarm processing blocks. For instance, when only the state-based alarming functionality is implemented, the alarm load shedding block can be omitted.

Additionally, the Event Broker service can be used in the application architecture, which means that instead of sending the output events (i.e. alarms or state changes) directly to the consumer of the service, they are sent to the Event Broker service, from which any subscriber can be notified about the arrived events. In this architecture, the services may be more loosely-coupled and there may be multiple event recipients (e.g. the state change can not only be stored by the system configuration service, but can be also propagated to the operator to check if this change is correctly in place).

Future Work and Conclusion

As the next step in the project, state-based alarming and alarm load shedding will be implemented to the process data generated by a Unisim model of an atmospheric distillation column [START_REF] Errington | Effective Alarm Management Practices[END_REF] shown in Fig. 4. This facility produces naphtha, kerosene, diesel, atmospheric gas oil, and atmospheric residue products from a heavy crude feed. The model is dynamici.e. comprising controllers and process specifications, such as the size of the distillation column trays and the side stripper tray sections, and the pressure flow. The incorporated dynamics allow for more realistic modelling of the facility behavior. Using this model, the process data will be generated by making changes to key process variables and also by simulating disturbances (e.g. failure of the reflux pump). The considered states for state-based alarming are shutdown, start-up, and different feed rate.

Based on the demonstration, we shall be able to assess the advantages and disadvantages of the SOA/CEP-based implementation for this type of applications. The main advantage of SOA lies in the loose-coupling of services which enables dynamic composition and orchestration. For CEP, it is the event-based functionality which yields improved performance and scalability. However, there are still a few open questions that need to be answered, such as how flexible the used CEP services really are, or if there will be any performance loss due to SOA or the Event Broker.

Fig. 1 .

 1 Fig. 1. State-based alarming for a shutdown state[START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF]

Fig. 2 .

 2 Fig. 2. State-based alarming software[START_REF] Hollifield | Alarm Management: Seven Effective Methods for Optimum Performance[END_REF]

Fig. 4 .

 4 Fig. 4. Dynamic Unisim model of an atmospheric distillation column with three side strippers

An alarm flood is a situation in which the operator is presented with too many alarms than he/she can effectively respond to[START_REF] Eemua | Alarm Systems, A Guide to Design, Management, and Procurement[END_REF]. Such situations are potentially dangerous, since the operator may overlook important alarms or assess the situation wrongly because of stress and information overload.

Acknowledgement

The research efforts described in this paper are co-funded by the European Union within the IMC-AESOP project (www.imc-aesop.eu). The authors would like to express thanks to the consortium partners for the fruitful discussions and cooperation.


Chi-Hyuck Jun
email: chjun@postech.ac.kr

Yun-Ju Cho

Hyeseon Lee
email: hyelee@postech.ac.kr

Improving Tree-based Classification Rules Using a Particle Swarm Optimization

Keywords: Classification, Data mining, Decision tree, Particle swarm optimization

The main advantage of tree classifiers is to provide rules that are simple in form and are easily interpretable. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining the splitting variables and their thresholds for a decision tree using an adaptive particle swarm optimization. The proposed method consists of three phases -tree construction, threshold optimization and rule simplification. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed method is promising for improving prediction accuracy.

Introduction

Decision tree is a popularly used data mining tool for classification and prediction. A decision tree usually handles one variable at a time and so it provides the classification rule that is easily interpreted. Decision tree has been widely used in many areas such as medical diagnosis, customer churn prediction, quality prediction and so on. Among many algorithms, CART [START_REF] Breiman | Classification and Regression Trees[END_REF]) and C4.5 [START_REF] Quinlan | C4.5: Programs for Machine Learning[END_REF] are the most famous and popularly used ones. Over the years, however, some studies show that constructing the optimal decision tree belongs to a non-deterministic polynomial-time complete problem [START_REF] Hyafil | Constructing Optimal Binary Decision Trees is NP-Complete[END_REF][START_REF] Naumov | NP-Completeness of Problems of Construction of Optimal Decision Trees[END_REF]. Therefore, most algorithms are greedy top-down approach [START_REF] Lior | Top-Down Induction of Decision Trees Classifiers-A Survey[END_REF]. When the algorithm searches for the splitting criteria, it considers only one variable at a time without considering the potential interaction between other attributes or variables. This approach has a risk of reaching a local optimal solution. To overcome this problem, multivariate splitting criteria are proposed by many researchers. However, the problem of finding the optimal linear split is more difficult and intractable than that of finding the optimal univariate split [START_REF] Murthy | Automatic Construction of Decision Trees from Data: A Multidisciplinary Survey[END_REF][START_REF] Lior | Top-Down Induction of Decision Trees Classifiers-A Survey[END_REF]. In the problem of finding the optimal univariate split, [START_REF] Athanasios | Breeding Decision Trees Using Evolutionary Techniques[END_REF] proposed the use of a genetic algorithm to directly evolve classification decision trees. The computational burden for genetic algorithms is substantially bigger than that for other algorithms. [START_REF] Saher | Anytime Learning of Decision Trees[END_REF] introduced a framework for anytime induction of decision tree, which generates the best answer up to the allowed time. However, it also suffers from the computational burden. Recently, [START_REF] Cho | Optimization of Decision Tree for Classification Using a Particle Swarm[END_REF] developed an optimization procedure of a decision tree using a particle swarm. But, they only optimize threshold values of variables chosen by the CART.

If we combine an existing efficient decision tree algorithm and an evolutionary optimization algorithm, computational burden can be reduced and more efficient decision tree can be obtained. This paper extends the work by [START_REF] Cho | Optimization of Decision Tree for Classification Using a Particle Swarm[END_REF] by considering an iterative way of finding better combination of variables and thresholds. The proposed algorithm consists of three phases; constructing a decision tree, optimizing the decision tree and rule simplification. First phase is to construct a preliminary decision tree by using well-known CART (classification and regression tree) proposed by [START_REF] Breiman | Classification and Regression Trees[END_REF]. Second phase is to optimize the preliminary decision tree by determining the optimal thresholds of splitting variables using an adaptive particle swarm optimization (PSO). The last phase is to simplify the rules by combining the regions having same class.

Adaptive Particle Swarm Optimization

Particle swarm optimization (PSO) is one of evolutionary computation techniques developed by [START_REF] Kennedy | Particle Swarm Optimization[END_REF]. As birds fly through a three-dimensional space, this algorithm makes use of particles moving in n-dimensional space to search for the optimal solution. PSO is a population-based iterative algorithm. The population consists of many particles, where each particle represents a candidate solution and moves toward the optimal position by changing its position according to a velocity. Let x    and v    be the position and the velocity, respectively, in the k-th variable of the i-th particle at the t-th iteration. Then, the updating formula for the position and the velocity of the i-th particle are as follows.

    =     +     (1)
    =  ×    +   ×   × (     -    ) +   ×   × (    -    ) (2)
where rand1 and rand2 are random numbers between 0 and 1. Here,     is the previous best position in the k-th variable of the i-th particle and     is the previous best position in the k-th variable among the entire population. We may omit the subscripts and the superscripts later if there is no risk of confusion. The parameter w is the inertia weight, and c1 and c2 are called acceleration coefficients which in the directions of pbest and gbest, respectively.

All the particles are evaluated by the fitness function at every iteration. The fitness function is used to evaluate the quality of the candidate solution. In the classification problem, accuracy, or mixture of sensitivity and specificity is usually used as the fitness function.

Like other evolutionary computation algorithms, the PSO requires some parameters as inputs. The size of the population, inertia weight (i.e., w), acceleration coefficients (i.e., c1 and c2), and the maximum number of iterations are parameters. To overcome the difficulty of parameter selection, [START_REF] Clerc | The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization[END_REF] first proposed an adaptive process. Several papers report that adaptive PSO enhances the performance of PSO in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability [START_REF] Clerc | The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization[END_REF][START_REF] Shi | Fuzzy Adaptive Particle Swarm Optimization[END_REF][START_REF] Xie | Adaptive Particle Swarm Optimization on Individual Level[END_REF]. In this paper, the algorithm developed by Zhan et al. ( 2009) is used for optimizing the decision tree. It identifies the current population state and proposes a control strategy of w, c1 and c2. The current population state is classified into four evolutionary state based on the evolutionary factor: exploration, exploitation, convergence, and jumping out. Before defining the evolutionary factor, the mean distance of the i-th particle to all other particles at each iteration is calculated by

d  =   ∑ ∑ x   -x        , (3)
where D is the number of variables and N is the number of population. Then, the evolutionary factor is obtained by

 =              ∈ [0,1] (4)
where d g is the mean distance of the globally best particle, d max is the maximum and d min is the minimum mean distance. The evolutionary state will be assigned by the fuzzy membership function and the control strategy for w, c 1 and c 2 is determined according to the rules of each evolutionary state in [START_REF] Zhan | Adaptive Particle Swarm Optimization[END_REF].

Proposed Algorithm

As mentioned before, most decision tree algorithms are greedy approach. They usually consider only one variable at a time when growing the tree. This approach may reach a local optimal solution. Most desirably, splitting criteria should be searched simultaneously. However, searching every possible combination requires a huge computation time. To avoid it, a three-phase hybrid procedure is proposed. First phase is to construct a preliminary decision tree by CART and to determine the splitting variables. But, the threshold values from CART are not used in the proposed method.

Only the number of threshold values will be maintained and the threshold values of each splitting variable will be re-determined in the second phase. Second phase is to optimize the preliminary decision tree by determining the optimal thresholds of splitting variables simultaneously using the adaptive PSO in [START_REF] Zhan | Adaptive Particle Swarm Optimization[END_REF]. The positions of particles in the adaptive PSO represent thresholds of split-ting variables. However, there are some differences in the way of classification between CART and the proposed method using the adaptive PSO.

Suppose that there are K splitting variables involved in the preliminary tree and that there are J  threshold values for the k-th splitting variable (k=1,…,K). Then, the number of variables to be considered in the proposed method is

å = = K k k J D 1 (5)
Also, the total number of cells to be partitioned in the adaptive PSO is (J  + 1)(J  + 1) … (J  + 1). The training data should be partitioned accordingly and the class prediction should be made in each cell. The class of a cell is predicted as the one having the largest observations among the training data partitioned. If there is no training data partitioned in a cell, predicted class may be determined randomly.

Basically, we may draw a separate classification rule for each cell partitioned in the proposed method. But, the classification rules can be simplified if some adjacent cells have the same predicted class. The third phase performs this. Based on this simplified rule, a new decision tree can be drawn if needed. It should be noted that the new decision tree may not have the same structure as the preliminary tree. The proposed algorithm can be summarized as follows.

Phase 1. Construct a preliminary decision tree by CART. [START_REF] Breiman | Classification and Regression Trees[END_REF] provide the detailed algorithm of CART, which consists of two steps.

Step 1-1. Grow the tree by splitting variables based on Gini impurity function until every node has a single (pure) class.

Step 1-2. Prune the tree based on cost-complexity and select the best pruned tree to obtain the preliminary tree.

Phase 2. Optimize the preliminary decision tree by the adaptive PSO.

Step 2-0. Initialize the positions and the velocities of particles at random. Set the initial gbest to the thresholds of the preliminary decision tree.

Step 2-1. Evaluate particles according to the selected fitness function.

Step 2-2. If the current fitness value of each particle is better than pbest, then update pbest value. If the current fitness value of population's overall best is better than gbest, then update gbest value.

Step 2-3. Update the parameters adaptively.

Step 2-4. Change the velocity and position of the particles according to the equations ( 1) and ( 2).

Step 2-5. If the maximum number of iterations is reached, stop. Otherwise, go to Step 2-1.

Phase 3. Simplify the rules by combining the adjacent cells having the same class.

Step 3-1. Represent each partitioning cell in terms of binary D-digits. Sort all cells in the ascending order of digits.

Step 3-2. Starting from the smallest digit cell, find the adjacent cells having the same class.

Step 3-3. Combine the adjacent cells having the same class Step 3-4. If there are no remaining cells for combining, stop. Otherwise, go to Step 3-1.

Note that the Phase 1 and Phase 2 may be repeated several times by selecting different variables to generate various trees.

Experiments and Results

To validate the proposed algorithm, numerical experiments are performed with an artificial data and a real data. Each data set is separated into two: the two thirds of the observations are used as a training set and the rest of one thirds are set aside as the test set. The number of particles and the maximum number of iterations are set to 100 and 500, respectively in the adaptive PSO. The initial values of (w, c1, c2) are set to (0.9, 2, 2). We compare our results against the original CART results using 5-fold cross validation to see the performance improvement through the proposed optimization method. In the proposed method, we use the classification accuracy as the fitness function.

Artificial Data Sets

The artificial data sets having three classes and three attributes are generated from 3dimensional multivariate normal distributions. Three different mean vectors and variance-covariance matrices are chosen as in Table 1. Three cases having different number of observations and different value of σ  are considered. Case 1 includes 100 observations for class 1, 150 observations for class 2, and 50 observations for class 3 (total of 300 observations), whereas σ  is set to 0.1. Case 2 includes 500 for class 1, 750 for class 2, and 250 observations for class 3 (total of 1500), whereas σ  is again set to 0.1. Case 3 is composed of the same class observations as Case 1, where σ  is set to 0.2.

Mean  0.1 -1.0 0.3   1.5 0.1 1.2   -0.5 1.5 0.5  Variance-Co variance σ  *  2 0 2 0 4 1 2 1 3  σ  *  4 1 1 1 3 3 1 3 4  σ  *  5 0 0 0 1 0 0 0 5  Table 2
compares the classification accuracies of the CART and the proposed procedure in the training and test sets for each of three cases. We randomly selected 2/3 of observations as the training set and the rest 1/3 as the test set. In fact, each number in a cell represents the average over 50 repetitions of experiments. It shows that the proposed method generally improves the classification accuracy as compared with CART. When the number of observations is increased as in Case 2 or σ  is increased as in Case 3, the proposed method outperforms the CART. The results are not reported here, paired t-tests were performed, which showed significantly difference in accuracies between the proposed method and the CART.

Real Data Sets

Three real data sets, that is, Parkinson's, Pima Indians Diabetes (Diabetes), and Blood Transfusion Service Center (Blood), were chosen for the application, which are available from UC Irvine Machine Learning Repository (http://archive.ics.uci.edu/ ml/). Table 3 shows the outline of these data sets. Table 4 shows the classification accuracy for each of the above three data sets. Performance of the proposed method is compared with that of the CART. We clearly see that the proposed method outperform the CART in all data sets. Particularly for Parkinson's and Diabetes data sets the proposed method significantly outperforms the CART.

Concluding Remarks

In this paper, we proposed the three-phase procedure of optimizing the decision tree.

Combining the CART algorithm and an adaptive PSO as the optimization tool, computational burden can be reduced and an improved decision tree can be obtained with the increased the classification performance. Particularly, we consider an iterative way of finding optimal combination of variables and thresholds as well as rule simplification. The performance was demonstrated through numerical experiments with artificial and some real data sets.

The proposed method can be applied to many quality classification problems in the area of production management. The quality of products from a manufacturing process can be classified into several grades and so a classification model can be built using the operation data from the manufacturing process. Using this type of model we can predict the quality of the final product in advance.

To improve the decision tree further, we may optimize the fully grown tree instead of a pruned if the computation cost is not severe. After optimizing the fully grown tree, we may eliminate redundant splitting criteria and prune the decision tree to get the user-specific size of tree. Furthermore, other variable selection method can be adopted when generating the preliminary tree instead of CART. If we can get a good set of variables critical to predict classes, then this will lead to a better classification rule. In this sense, combining random forest and the adaptive PSO may be a good alternative. Similarly, other optimization algorithm such as genetic algorithm, ant colony optimization, or simulated annealing, may be used. Optimization of decision tree having multiway splits is also an interesting future area for study.

Table 1 .

 1 Mean vectors and variance-covariance matrices for generating artificial data

	Class 1	Class 2	Class 3

Table 2 .

 2 Comparison of Accuracy for Training and Test Data (in percentage)

		Case 1		Case 2		Case 3
		train	test	train	test	train	test
	CART	95.86	93.41	92.57	89.61	89.28	81.52
	Proposed	97.43	92.59	96.18	93.32	94.53	83.98

Table 3 .

 3 Outline of Three Real Data Sets

		Parkinson's	Diabetes	Blood
	# classes	2	2	2
	# observations	195	393	748
	in training set	130	262	499
	in test set	65	131	249
	# attributes	22	8	4

Table 4 .

 4 Classification Accuracy in Three Data Sets

		Parkinson's	Diabetes	Blood
	Proposed method
	Training set	100	91.6	83.0
	Test set	89.2	72.5	77.1
	CART
	Training set	96.9	86.6	81.2
	Test set	84.6	67.9	76.7

Acknowledgement. This work was supported by Basic Science Research Program through the NRF funded by the MEST. (Project No. 2011-0012879)