Yang Xu

Jing Xu

Alain Bernard

Knowledge Management in E-commerce Mass Customization

Keywords: Knowledge management, Mass customization, modeling

Mass customization is one of the leading strategies in satisfying customers and assuring companies survival in today's markets. From the point of view of lifecycle management, it includes knowledge of customer requirement, customer preference, raw material purchasing, customization manufacturing process, product price which reflects satisfaction of both sides of bargain. This paper proposes a knowledge management model in E-commerce mass customization, which is consisted of three parts: the customer information gathering model, the purchasing and manufacturing model and the pricing model. The modeling process and further analysis show that the model efficiently integrates different knowledge in mass customization lifecycle and can serve as a useful platform in Ecommerce.

Introduction

In this era characterized by constantly changing environment, rapid technology progress and fierce market competition, knowledge is recognized as a manageable key resource for enterprises to survive and develop [START_REF] Grover | General perspectives on KM: fostering a research agenda[END_REF][START_REF] Bernard | Methods and tools for effective knowledge lifecycle management[END_REF]. Knowledge, also known as intellectual capital, has been regarded as one of the sustainable assets, so people are paying more and more attention in knowledge management issues (Xu et al., 2011). Knowledge can add value to products/services so as to help enterprise make profit, and knowledge management aims at figuring out how to add value as much as possible within a given cost (Xu and Bernard, 2010). Although the strategic value of knowledge is clear, in most cases people have not mastered the art of managing knowledge. Thus, it is quite useful to make a profound study on knowledge management in a given business model, for example, mass customization.

Mass customization (MC) is usually referred as a term as an oxymoron of mass production and customized goods [START_REF] Kaplan | Toward a parsimonious definition of traditional and electronic mass customization[END_REF], and it has become an undisputable reality that MC is one of the leading strategies in satisfy-ing customers and assuring companies survival in today's markets [START_REF] Daaboul | Design for mass customization: Product variety VS process variety[END_REF]. Mass customization makes high value-added products/services, and enhances profitability by reducing the costs of production and logistics and better satisfying the customer personalized requirements (Jian et al., 2003;[START_REF] Grenci | Maximizing customer value via mass customized e-consumer services[END_REF]. When an enterprise is able to offer personalized or customized products, customer is then involved in the product design process, which may increase the value of the product -customer perceived value (CPV) [START_REF] Gautam | Lean product development: maximizing the customer perceived value through design change (redesign)[END_REF]. One of the most distinguished features of mass customization is to provide customers with the possibility to co-design products/services according to their personalized preferences and interests. Providing customers with the ability to co-design products/services based on their own preferences has been considered one of the most distinctive features of mass customization [START_REF] Ogawa | Reducing the risks of new product development[END_REF]. Products/services can be considered to be an integration of different modules, and different features of these modules can satisfy customers' needs and provide value. From this point of view, mass customization can be regarded as a method to provide customers with a choice menu based on market segmentation [START_REF] Fogliatto | Mass customization: A method for market segmentation and choice menu design[END_REF].

Implementing mass customization in pure traditional manufacturing industrial is quite difficult, so it is usually integrated with information technology (IT) systems to form E-commerce solutions. E-commerce can provide the linkage to capture external information and it is regarded as a possible solution for mass customization [START_REF] Helms | Technologies in support of mass customization strategy: Exploring the linkages between e-commerce and knowledge management[END_REF]. E-commerce uses computer and networks to do business, such as buy and sell products/services, transmit information, etc. With the rapid progress of information technology, people are award of the fact that when a business model is created, built and applied properly, E-commerce would facilitate mass customization in many aspects [START_REF] Turowski | Agent-based e-commerce in case of mass customization[END_REF][START_REF] Helander | Research on E-product development (ePD) for mass customization[END_REF]. Meanwhile, E-commerce based on the rapid progress of advanced Information and Communication Technologies (ICT) should be linked with effective knowledge management strategies. Knowledge gained and managed via e-business can enhance customer relationship management, supply chain management, product development, etc. [START_REF] Fahey | Linking e-business and operating processes: the role of knowledge management[END_REF] In order to better explain the proposed theoretical model, this paper takes mass customization on food menu in E-commerce as example. With the rapid growth of people' requirement on food, people do not only care about nutrition and safety issues but also need more customized menu. There exists "special supplied food" which can be ordered by customers to fit their personalized requirements, but they are quite expensive because there is a lack of mass production. Scale effect is one of the primary means to reduce costs [START_REF] Easton | Scale and the Scale Effect in Market-based Accounting Research[END_REF], so mass customization should be applied to balance the two sides of scale effect and customized requirements. As a result, the main problem to be solved is that, to what extent the benefit brought by customization can compensate the increased cost caused by quantity reduction, in other words, what to customize and how to customize. This paper mainly focuses on knowledge management issues in E-commerce mass customization and proposed a model for real case implementation.

Modeling process

Mass customization is applied by more and more enterprises to attract customers and make more profits. A survey based on market investigation [START_REF] Xu | A survey on mass customization in restaurant industry[END_REF] shows that customers are willing to pay about 50% more to get a customized diet menu (different collocations of the food materials, to what extent the food is cooked, flavors, etc. can be determined by customers) rather than homogeneous fast food supply. Another results got from the investigation is that customers are willing to pay more for personalized requirements but they would like to pay extra fees as little as possible. Consequently, an obvious conclusion can be made: enterprises which can apply mass customization to provide customized product and/or service with a lower price will win in the fierce market competition.

The integrated model is consists of three parts: the customer information gathering model, the purchasing and manufacturing model and the pricing model. Figure 1 shows the framework of the mass customization lifecycle management model.

Customer information gathering model

In the phase of customer information gathering, seven modules are constructed as follows. 1) Information announcement on the official website. The company announces the types of food that it will accept to be ordered in the next time period, for example, in the next month. Customers may choose one or some of them according to their preferences, determine possible attributes of the food they will order, and illustrate customized package. This module may integrate official website announcement, channel marketing, email pushing, etc. 2) Customer account management. Each customer may have an account, which will be a base of the customer's information. This module may include registration, login, logout, edit, administrator management, etc. 3) Customized design by the customer himself/herself. Customers choose the foods they need for their menu according to the choices given by the company website, and determine the possible combination. For example, if a customer choices milk as a part of the menu, he may determine its value: sugar percentage, fat percentage, etc. In order to control the complexity of customization, attribute values can be restricted if needed, for example, for the possible value of fat percentage of milk, only whole milk, skim milk and half skim milk are available. 4) Weight assignment. The customer should assign different weight values to each customized choice according to their preference. To facilitate later processes, the sum of all weight values is 1. These weights reflect the priorities of customers' preferences. 5) Customer order processing. This module will be discussed in detail in the following sections. 6) Customer feedback. This module enables customers to give some feedbacks to the company, which are valuable information. 7) After sales service. This module can be regarded as the end of the mass customization lifecycle. Like product lifecycle management, mass customization management has also an end, but it can also be treated as the beginning of a new lifecycle. These seven modules fundamentally consists a whole lifecycle of customer information gathering process, c.f. Figure 2. During its implementation in the real world, companies are expected to realize customer relationship management in order to encourage new orders from the existing customers, so as to form a virtuous lifecycle of mass customization.

Purchasing and manufacturing model

In the phase of purchasing and processing, three modules are constructed as follows. 1) Purchasing general components based on prediction. General components refer to foods in their original status, such as pure milk with any skimming treatment. General components are obtained directly from raw materials. In this phase, the purchasing amount are forecasted based on existing stock, order data of the same period in the past years, etc. 2) Purchasing customized components based on customer orders. After receiving orders from customers, the company will purchase customized components. 3) Semi-manufactured food processing. Semi-manufactured foods are made according to the customer order and purchased material. For example, 100L of calcium-fortified milk are produced with pure milk (purchased in phase 1) and calcium additive (purchased in phase 2). The amount of semimanufactured food is determined by orders. 4) Customized manufacturing process. After producing all kinds of semimanufactured food, the company assembles them for a customized menu ordered by customers. Figure 3 shows the purchasing and manufacturing sub-model.

Pricing model

Pricing issue is crucial for mass customization [START_REF] Sundararajan | Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing[END_REF]. Unsuitable pricing strategy will prevent companies to gain their maximum profit and customers are not willing to pay an unreasonable price for the customized goods either. In our model, four parts form the pricing strategy for the customized orders:

1) The fundamental price. This part is determined by the minimum costs of all raw materials.

2) Price for customization. Customization needs extra cost compared to homogeneous order. 3) Price reduction to compensate the unaccomplished parts of the customized order. When the customers required a customized order and are willing to pay a high price, they deserve a complete product/service. If the company could not accomplish the task, they should be punished. In other words, the company should take the risk brought by mass customization as well as the profit brought by it. 4) Extra cost caused by order revision. Customers may not be satisfied with their order or the price matching their order. As customer orders have impacts on the mass customization process, such as purchasing processes and the pricing processes, some costs will happen each time a customer order is processed. As a result, for the customer side, they should undertake a higher price if they adjust their customized orders. Here are some parameters used in the pricing sub-model.

 ( , ∑

). This is the will point that customers assigned to each customized need. is the number of customized needs. It is reasonable that when the customer has a high requirement in some aspects, he/she should reduce his/her expectations in some others. Mass customization does not mean all customer requirements have to be satisfied, which is unreasonable in the real world where everything has a budget.



. This is the lowest purchasing cost of each raw material.



. This is the cost of each possible component of an order. For example, for a customized order for a breakfast menu, there is different components and the component has different options. According to different requirements, different options of a given components may have different costs.



. If any customized requirement cannot be satisfied, the price should be reduced to compensate the customers. is the amount reduced for each will point. For example, if a customer has an personalized requirement for the sugar percentage of the coffee and gives it 10 will points, but the company cannot satisfy (or feel it worth not to be satisfied), the price of the order should reduce . 

. This is the number of order matching round. For example, when a customer proposes a customized order for the first time,

. If the customer is not satisfied and choose to adjust the order for a second round, . 

. If the customer adjusts his/her customized order, there will be an additional cost, which is . For example, when the customer adjusts his/her order and resubmits it for the time, there is an addition of on the price he/she will pay.



. This is a 0-1 variable, which is used to judge whether a given customized requirement has been realized.

{



. This is the expected interest of the customized order. can be obtained by statistical and/or forecasting method using historical data of the company.

As a result, the customized price is calculated as follows.

[∑ ∑( ) ] ∑
Pricing procedure is shown in Figure 4. Figure 4 shows that the pricing procedure firstly figures out the lowest cost of all raw materials used for the order, i.e. . The addition of is the fundamental price of the order. Then, in the assembly process, the model records what is actually used to form the menu order and thus determine all parameters to calculate the price. Using the proposed calculation equation, the price is calculated. At the same time, the difference between the menu provided and the original order of the customer is sending back to the customer, and ask the customer whether he/she agrees with the menu provided and the correspondent price. If the cus-tomer agrees, the procedure arrives to a happy ending. If the customer does not agree and does not want to continue the order, the order will be canceled automatically. If the customer does not agree with the menu provided and its price, but would like to make a customized order for a second round, the parameter will be adjusted to , and a new process will begin.

Discussions

One of the key issues in the proposed E-commerce mass customization model is how to design the choice menu for the customer. Customization does not mean to satisfy all customer requirements without any constraint. Not only the production cost or manufacturing activities should be controlled, but the customer requirements should also be controlled. In other words, customization in our model does not refer to original creativity of customer, and the range of customization is controlled by the company, so as to make the E-commerce platform realizable and efficient. The design of customized choice menu should take former orders into account and consider current stock situation as well. Once a customer order is processed, it will have impacts on the following purchasing activities and the design of choice menu. During the modeling process, the purchasing of general components is determined by the forecasting and the stock, and the purchasing of semi-manufactured components is determined by orders. In fact, orders reflect to what extent the choice menu satisfy customer satisfaction. As not all customer requirements are satisfied, online choice menu can be adjusted according to the feedback reports from the pricing model. Furthermore, the design of choice menu should also take current stock situation into account, so as to reduce the stock. For example, if the company has some overstock beefsteaks, some options providing menu with beefsteak could be added to the choice menu, so as to increase the probability of consumption of those overstock materials.

Another crucial issue processed by the model is how to customize the product price. According to the model, production costs of each order are not the same, so as for the purchasing strategies. The pricing model takes the fundamental costs, costs for customization and the degree of customization satisfaction into account. As a result, different choices made by different customers at different times vary a lot, so the product prices are customized.

In order to increase the impacts of customers' preference and control customers' requirements in a reasonable range, customers are required to weight to their different options during the customer information gathering model. On one hand, companies may have a deeper understanding of customers' preferences which can help companies to adjust their commercial activities; on the other hand, customers' preferences are better respected. For example, if an important preference (given a relatively high weight) of a customer cannot be satisfied, more compensation are given to the customer by reducing the customized price.

Conclusions

This paper has proposed a knowledge management model in E-commerce mass customization. The proposed model is consisted of three parts, the customer information gathering model, the purchasing and manufacturing model and the pricing model. The model integrates knowledge about customers' requirements and preferences, customization processes and product prices, so as to provide reasonable products/service with a suitable price. The model also analyzes knowledge management issues in mass customization from the point of view of lifecycle management, from information gathering, material purchasing, customization manufacturing and feedbacks. In this integrated process, not only the company but also customers are involved, especially performed in the pricing model.

Further research opportunities could include more consideration on customer relationship management and supply chain management so as to integrate more knowledge related to the mass customization lifecycle.

Figure 1 .

 1 Figure 1. Framework of the mass customization lifecycle management model

Figure 2 .

 2 Figure 2. Customer information gathering sub-model

Figure 3 .

 3 Figure 3. Purchasing and manufacturing sub-model

Figure 4 .

 4 Figure 4. Pricing procedure

Acknowledgement

This work is supported by the National Social Science Foundation of China (No. 13CTQ022) and the National Natural Science Foundation of China (No. 51205344).


Sébastien Nila
email: sebastien.nila@gmail.com

Frédéric Segonds
email: frederic.segonds@ensam.eu

Nicolas Maranzana
email: nicolas.maranzana@ensam.eu

Dorothée Crepe
email: dorothee.crepe@keonys.com

International Conference on Product Lifecycle Management Deployment of Knowledge Management in a PLM environment: a software integrator case study

Keywords: PLM, Knowledge Management, methodology, software integrator

The past years have seen growing investments in the area of PLM by several industries. In today's industrial production, PLM is an essential tool to cope with the challenges of more demanding global competition and ever-shortening product lifecycles. Complex products require collaboration of large specialist networks. Knowledge Management (KM) can be apprehended in two manners: the defensive manner builds the stock of knowledge to face the departures of personnel, or, the offensive manner sees in the knowledge development an advisability to generate new products. The link between PLM and KM is interesting as it can help answering "on field" problems. In this paper we first make a state of the art of knowledge and KM in a PLM context. Then we propose a methodology to deploy KM in the particular case of a software integrator. Finally, we propose an experimental protocol that will allow us to improve a tool demonstrator in an agile way.

Introduction

PLM systems are nowadays widely used in engineering design. The economic context forces industries to achieve more and more ambitious projects with ever shortening time and money. On the other side, knowledge is the most important thing in a company, and is mainly stored in the employees' mind, as illustrated in Figure 1, extracted from [START_REF] Segonds | Contribution to the integration of a collaborative design environment in the early stages of design[END_REF].It seems worth remembering that most of the knowledge can't be stored on a computer, because it is the fruit of Human. Design choices made are often implicit and very few are archived in any software. This kind of knowledge is, at the moment, difficult to store and manage. The progressive integration of KM in PLM systems is means to improve knowledge spread.

Figure 1: distribution of knowledgeinproduct design, extracted from [START_REF] Segonds | Contribution to the integration of a collaborative design environment in the early stages of design[END_REF] In this paper, we first make a state of the art of knowledge and KM in a PLM context. Then we propose a methodology to deploy KM in the particular case of a software integrator. Finally, we propose an experimental protocol that will allow us to improve a tool demonstrator in an agile way.

PLM context and associated challenges 2.1 Evolution of design methodologies and dedicated software tools

In a context marked by increasing competition, businesses must suit their organization to the demands of their customers. In this context, the reduced duration of development cycles and the increasing complexity of mechanical systems force businesses to involve actors from various professional and cultural backgrounds in collaborative projects. The organization of design teams has also had to adapt to these changes in the industrial context. Figure 2 illustrates the changing patterns in the formation of new product development teams as these moved to greater collaboration and virtuality.

Figure 2

Changes in design teams adapted from [START_REF] Sharifi | Product Development Strategies for Agility[END_REF] Obviously, these industrial evolutions have been supported by evolutions in work methods and in the associated digital tools, such as PLM solutions. At the same time, research is actively being carried out to develop methodologies and technologies to support geographically dispersed teamsin order to facilitate product development processes (Dodgson et al.,2006). The objective is to organize collaborative work based on the rapid evolution of information technologies. Research works and commercial systems have appeared to provide solutions for collaborative and distributed product development, and the practical applications are getting more and more mature [START_REF] Li | State-of-the-art technologies and methodologies for collaborative product development systems[END_REF]. In an attempt to improve productsand reduce costs and time to market, concurrent engineering (Solhenius, 1992) or lifecycle engineering has emerged as an effective approach to address these issues in a competitive global market [START_REF] Asiedu | Product life cycle cost analysis: State of the art review[END_REF].Influenced by the NTIC development, global economic challenges, and decentralized structures, the objectives and scope of Product Data Management technology (PDM) have changed. Thus, in the early 2000s, PLM emerged as a solution to adapt industrial design to the demands of globalization. Indeed, as PLM addresses the entire lifecycle of the product, it has a cross-functional nature and deals closely with the way a company runs [START_REF] Garetti | Organisational change and knowledge management in PLM implementation[END_REF]. Collaborative design has been the subject of numerous studies. With the development of PDM, PLM and associated workflows, software firms have proposed solutions to the everyday problems of engineering design departments (versioning of documents, naming etc.). The PLM approach can be viewed as a trend toward a full integration of all software tools taking part in design and operational activities during a product life cycle [START_REF] Garetti | Organisational change and knowledge management in PLM implementation[END_REF][START_REF] Donati | PLM platform: integrated support of the entreprise digital chain for Collaborative Product Development[END_REF]. Therefore, PLM software packages need product data management system; synchronous and asynchronous, local and remote collaboration tools; and if necessary, a digital infrastructure allowing exchanges between software programs.These systems are distributed technological information systems for archiving, administrating and providing all product or facility related information in required quality and at the right time and place (Ameriet al., 2005): they are nowadays real key points for companies businesses.

PLM systems: key points for business

A product is a complex "fabricated-assembled" element, comprising a large number of components, functions and process steps (Clark et al., 1991). To be or stay competitive, anindustry needs to be different than others (Danneels, 2002), that's why most of industries are in a transformation step to be more responsiveness about customers and competitors. PLM is, for a long time, considered as a key for business and the transformation of engineering processes (Rekieket al., 2002). Moreover, PLM solutions are efficient tools to store knowledge and facilitate their re-use. In the next section, we will discuss about the integration in PLM systems of expertises generated all along projects, also known as knowledge.

3 Knowledge Management and PLM

Knowledge Management

According to [START_REF] Wiig | Knowledge management: where did it come from and where will it go?[END_REF], knowledge is information combined with experience, context, interpretation and thinking. It is a high value form of information that is ready to apply to decisions and actions. Simply put, knowledge can be defined as the integration of ideas, experience, skills that have the potential to create value for a business by informing decisions and improving performance. In this view, knowledge is a key enabler to organizational success. However, in order to be useful, knowledgemust be available, accurate, effective and accessible. In NewProduct Development (NPD), there is an implicit distributed interaction among different actors.As we enter the knowledge society, ownership of knowledge and information as a source of competitive advantage is becoming increasingly important. In other words, organizations depend more on the development, use and distribution of knowledge based competencies. Consequently, organizations are focus more attention to the concept of managing their knowledge base in order to increase competitive advantage, through effective decision making and innovation [START_REF] Nonaka | The knowledge-creating company: How Japanese companies create the dynamics of innovation[END_REF][START_REF] Davenport | Improving knowledge work processes[END_REF][START_REF] Sveiby | The new organizational wealth: Managing & measuring knowledge-based assets[END_REF]. Knowledge is a key resource that must be managed if improvement efforts are to succeed and businesses are to remain competitive in a networked environment [START_REF] Gunasekaran | Agile manufacturing: a framework for research and development[END_REF]. Indeed, it is a big adds value for organizations to capitalize on knowledge sources by trying to predict how the new product will perform in an unknown context. From the social perspective, the challenge consists in sharing knowledge and interconnecting people that are imagining these future conditions. Managing knowledge is about creating an environment in continuous creation, aggregation, use and reuse of both organizational and personalknowledge in the pursuit of new business value. KM can be considered as a systematic and organized attempt to use knowledge within a company to transform its ability to generate, store and use knowledge in order to improve performance. In short, the leading purpose of KM is to make knowledge accessible and reusable to the organization. As PLM, KMhas a true add value. As we will discuss in next chapter (4), these two methods (and dedicated tools)can be combined in order to deliver knowledge all along the lifecyle of a product.

PLM and KM integration

Currently, there is alack of studies on information flows needed acrossproduct lifecycle operations.Thus, the unavailability of explicit flows leads to a certain degree of inefficiency in performing lifecycle operations. Methods dedicated to efficiently represent, control and search information flows are critical. KM requires the identification of information flows and their efficient management, which can play an important role in analyzing and taking decisions during the product lifecycle [START_REF] Jun | Several aspects of information flows in PLM[END_REF].As Ouertani et al. (2011) mentioned, querying and sharing product knowledge is becoming a key issue in enterprise. Hence, the success of PLM and KM integration lies in identifying what kind of information are available in the other phase, and how we can use them in order to streamline business processes. An emergent challenge consists in providing a context-driven access to federated information and knowledge, fostering cross-discipline collaborations between actors to improve quality in product development. In the next section, we will try to address this challenge by proposing a methodology to deploy KM in PLM environment.

4.

Proposition of a methodologyto deploy KMin PLM environment 4.1. Industrial context: the Keonys company

The context and problem of identifying and thereafter representing, analyzing and managing information and knowledge in an organization has always been very crucial to achieve business goals in an efficient and flexible way. Particularly in a PLM context, the issue of information overload is growing in importance. Among the existing integrators, Keonys the European leader in the integration of PLM solutions is in constant development, both in its workforce as its revenue and services offered to customers. In the same way the company's knowledge, defined as expertise and know-how, is in growth and is divided in different branches of the group in Europe.In this growing environment, Keonys is looking for a way to capitalize on the knowledge of the company and arrange them in different ways in order to define Best Practices. The purpose of this paper is to propose a methodology which can answer the needs of development of Keonysthrough the integration of KM in PLM environment. It includes several points, from the categorization of company knowledge to the identification of an adapted Knowledge Based Engineering System [START_REF] Sriram | Artificial intelligence in engineering: Personal reflections[END_REF]).In the next section, we describe the proposed methodology.

Proposed methodology to deploy KM in PLM context

Agile methodology is an alternative to traditional project management, typically used in software development. It helps teams respond to unpredictability through incremental, iterative work cadences, known as sprints. Agile methodologies are an alternative to waterfall, or traditional sequential development.

Agile development methodology provides opportunities to assess the direction of a project throughout the development lifecycle. This is achieved through regular cadences of work, known as sprints or iterations, at the end of which teams must present a potentially shippable product increment. By focusing on the repetition of abbreviated work cycles as well as the functional product they yield, agile methodology is described as "iterative" and "incremental." In waterfall, development teams only have one chance to get each aspect of a project right. In an agile paradigm development, requirements, design, lifecycle. The main advantage is its flexibility during the development. This method is oriented on code development and ensures an adapted implementation of the functionalities. It tends to improve how software and process are developed. Segonds (2011) developed a g environment by integrating an agile development method. We base on this model to develop a methodology attempting to integrate KM approach in the state of art (Figure 3

Integration of recommendations

functional product they yield, agile methodology is described as "iterative" and "incremental." In waterfall, development teams only have one chance to get each aspect of a project right. In an agile paradigm, every aspect of development, requirements, design, is continually revisited throughout the The main advantage is its flexibility during the development. This method is oriented on code development and ensures an adapted implementation of the functionalities. It tends to improve how software and process are developed. Segonds (2011) developed a generic model based on a collaborative environment by integrating an agile development method. We base on this model to develop a methodology attempting to integrate KM approach seen in the state of art (Figure 3). PLM interface model design with KM integration and agile development, adapted from Segonds (2011).

Development of a generic methodology adapted to Keonys' needs

Analysis of the existing methodologies for KM

Extraction of Knowledge

Use Case based on industrial scenarii

Assessment of KM methodology

Results analysis and user feedback of recommendations

Tool prototype design

Incremental and iterative development in PLM context

Considering the case of Keonys, this company is oriented in a standardization development strategy, they want to create generic application in PLM tools to make an add value and to be different from their competitors.The group is separated in different work places. This method is a good way to develop a prototype tool adapted to the way of work of this company. With the consulting activity, it's difficult to group all developers in the same moment to adapt a methodology. The incremental and iterative development help to develop the methodology in asynchronous time.

Experimental knowledge extraction and methodology test

The first phase of the proposed methodology is one of the most important as it allows the identification of knowledge in the company. To identify knowledge, user interviews are planned with different experts. The user interview is a method used to collect oral data from individuals or groups in order to derive information from specific facts or representations. The relevance, validity, and reliability of this information are assessed based on the goals of this data collection. The main types of interviews include the directed interview, the semi-directed interview, and the free interview [START_REF] Blomberg | Ethnographic field methods and their relation to design[END_REF]. Considering our goals, the type of interview that seems to suit our needs best is the semi-directed interview. It allows us to collect precise data in a reasonable length of time and fosters a genuine dialogue between the interviewer and interviewee. The second phase is the analysis of the existing methodologies of KM to inventory functionalities. Then, in order to test the reliability and validate the proposed methodology, user tests will be run with experts and novices. According to Nielsen and Laudauer (1993), no more than ten participants are needed for a usability tests. The recommendations made during the tests will help us to develop a tool prototype in an incremental and iterative way to fulfill the requirements expressed by the users.

Results

At this moment of study, the three first phases of the Agile methodology has been treated.

4.4.1Knowledge extraction

For the extraction knowledge phase, a first approach was to target people who are in charge of developing activities. After an overview of the company we selected ten employees in three different entities, a questionnaire was created for a first approach. The questionnaire goals is to refer project, industry, type of development, time spent. The result of this questionnaire show a first difficulty to manage people to answer correctly and in time, it comes from the volatility of concerned people and from their manager to push them. The data collected with the questionnaire is well organised and structured. Every project referenced is followed by the development code, use instruction and setup instruction. This first approach is a good way to get direct or phone contact and speak more easily to developer if there is missing explanation or missing data in the questionnaire. After this step all developmentwas tested and analysed to add new information's like usability, adaptability, pertinence. All information collected by questionnaire, interviews, test and analysis helped to categorize data and put priorities. The second phase of the methodology is to analyse existing KM methodology in the company. There is four main tools using KM (Opportunity Review Business, Enterprise Resource Planning, Service Request, Intranet). These tools serves the company to capitalize data and information in an end to end way, from the opportunity to the billing. After a deep analyse of these tools, there is not the possibility to use one of them or a part of one of them to answer to Keonys needs concerning the development activity.

Conclusion

This article proposes to integrate KM approach to improve PLM systems. The state of art demonstrates that evolutions in design methodologies and dedicated software tools have promoted PLM and KM as key points for companies businesses. The presented methodology combines agile software development in PLM context. KM will allow us to provide a tool prototype dedicated to Keonys company. The extension of this method to others companies could increase user's satisfaction and, as a consequence, the efficiency of the company through the use of KM integrated in PLM environments.

Figure 3 .

 3 Figure 3. PLM interface model design with development


Nicolas Drémont
email: nicolas.dremont@utc.fr

Nadège Troussier

Robert Ian Whitfield

Alex Duffy

A meta-model for knowledge representation integrating maturity for decision making in engineering design

Keywords: preliminary collaborative design, maturity, PLM, decision making, uncertainties

Computer Aided Design (CAD) and Computer Aided Engineering (CAE) models are often used during product design. Various interactions between the different models must be managed in a PLM approach for the designed system to be robust and in accordance with defined specifications. To effectively manage engineering changes on the system definition, the dependencies between the different models must be known and managed in a PLM system. A lot of data are exchanged in collaborative design and different problems must be managed in terms of data consistency. We propose a metamodel for knowledge representation integrating maturity concept to support decision making in preliminary collaborative design. A literature survey about existing knowledge models is done before presenting the proposed meta-model of knowledge.

Introduction

The design process is complex and dynamic due in part to the volume of handled data and models, the number of exchanges between the different design teams and businesses interacting. The design teams, organized in Concurrent Engineering (CE) do not wait to get the result of the later phases of the design life cycle; they anticipate them by making assumptions and by taking into consideration previous experiences and know how. In that framework, quality approaches for the control of product performance, and collaborative engineering tools to support CE and collective decision making are required.

Product development cycles, and more generally product life cycles are becoming increasingly complex. This complexity is due to several aspects. One of them is related to the different levels of representation and modeling due to the organizational and technical decomposition of the technical system, the inter-relations between different kinds of knowledge involved to anticipate the product's behavior. Another one can be associated to with the necessity to integrate different viewpoints, creating problems related to the data consistency and considering the impact of engineering change. It is therefore necessary to be able to qualify and quantify the data in the upstream phases of product design [START_REF] Pahl | The Ideal Engineering Decision Support System[END_REF] and throughout the design process in order to help to the next step decision making [2].

In order to support decision making in early design and product's performance management, this paper proposes a meta-model for knowledge representation integrating maturity to support decision making in preliminary collaborative design. Section 2 presents an overview of the problematic of decision making in preliminary collaborative design. Section 3 presents a literature survey about knowledge metamodeling approaches. Finally, Section 4 describes the different elements of the proposed meta-model 2 The decision making in preliminary collaborative design Ullman [2] defines the decision pyramid in several levels (Data, Models, Knowledge and Decision). Decision making requires the management of data, models and knowledge, and the associated judgment on which decisions are based. In other words, data are the pyramid basis and also the basis of decision making in design (section 2.1). Moreover the collaborative aspect includes different representations of a same mechanical system, oriented with respect to the expertise domain considered. These representations are supported by knowledge and product models.

Decision making and lack of knowledge

Decision making in preliminary collaborative design involves the selection of an alternative design in order to go towards the next design iteration. Several factors are considered in order to make a decision, such as market demand, design alternatives, designer's preferences and uncertainties [2]. We focus on the "uncertainties" factor because it can represent the lack of knowledge in the decision making (for epistemic uncertainties). The decision making enables us to get a new definition of the mechanical system and we assume that maturity level and uncertainties on the product design data facilitate the next design decision. Maturity level is a characteristic often used to qualify information in design [START_REF] Besharati | A decision support system for product design selection: A generalized purchase modeling approach[END_REF]. It can be defined as the improvement degree through a predefined set of process domains in which all objectives of the set are completed [START_REF] Grebici | Framework for Managing Preliminary Information in Collaborative Design Processes[END_REF].

To design a mechanical system means to integrate several technologies with strong interactions and to take into consideration different aspects such as mechanical, electronic, etc. Moreover, in a collaborative or an extended enterprise context, several people must work together in order to design efficiently a mechanical system [2]. This collaborative aspect is very important because each person has a specific point of view and way to thinking but these people must take decisions together in order to meet compromises and to be able to go to the next design iterations repeatedly until the design objectives and technical specifications are achieved.

The preliminary design in collaborative design of mechanical system provides still more difficulties because the mechanical system is being defined [START_REF] Besharati | A decision support system for product design selection: A generalized purchase modeling approach[END_REF] [5] [START_REF] Pahl | The Ideal Engineering Decision Support System[END_REF]. It means that uncertainties about design data and unknown data have to be considered.

Product and knowledge representation

Design can generate many models (geometric and simulation for example 3D geometric representations, FEA models, etc.), with respect to the behavior which is to be studied, the component and configuration of the product, as illustrated by Scheidl and Winlker [START_REF] Blessing | Acts of: The role of design in the shaping of technology, COST A3/ COST A4 International research workshop[END_REF] on a beam, where the different models are clearly in the conceptual design phase. Another reason for the model diversity is related to the complexity of actual systems being developed [START_REF] Scheidl | Relation between conceptual design and detail design[END_REF]. These systems are characterized by independent functionalities that, together, compose the product (systems of systems). Complex systems are an association of several functionalities using diverse technologies to achieve the required operation of the product. During the design phases, the models that are used aim at providing a representation of the product in terms of its physical description (geometric model) as well as behavioral description (simulation model).

Thus, the design of complex systems can necessitate a significant number of models, specific for each discipline and that require a multiple views approach. Different engineering domains require different viewpoints on the product with different levels of granularity. For instance, within an electro-mechanical product, the structural decomposition depends on the engineering domain of the expert analyzing the product: an electrical model considers the gaps between parts while mechanical analyst does not mind about these, and typically they will not use the same product decomposition [START_REF] Mtopi-Fotso | Design of product families based on a modular architecture[END_REF]. In terms of data and process modeling, several product models exist to support the multiple view representation of the system and will be described Section 3, in order to support product lifecycle management and the collaborative decision making process. Moreover, in order to support knowledge mapping and to ensure consistency between different models, meta-models can be proposed using generic semantic and rich representation of concepts and relationships between them. The goal is to propose a conceptual framework that facilitates the definition of heterogeneous knowledge models integrating the maturity in order to help to the decision making.

Maturity, data qualification and knowledge models 3.1 Definitions

We define maturity based on the work of Beth [START_REF] Grebici | Framework for Managing Preliminary Information in Collaborative Design Processes[END_REF], as the association of the knowledge and performance. This means that there is the judgment of an actor on information (transmitter and receiver) and the state of information from actor user of information must be taken in consideration.

Performance is the link between specification of the product and the specification achieved in the current design iteration [START_REF] Noel | Specification of a product modelling concepts dedicating to information sharing in a collaborative design context[END_REF]. If no specification is respected then the performance is null and if they are all achieved then the level is of 100%.

We define knowledge as a cognitive structure allowing interpreting a set of information in order to follow a reasoning in a particular situation (or context of use) and for a stated purpose [START_REF] Boucher | Towards the competency integration in performance piloting of enterprise[END_REF]. The lack of knowledge, in this case, is represented by the uncertainty on parameters of the product, for example the uncertainty of the part diameter, more or less 10 millimeters. Designers and user of the parameters define this uncertainty. A type of uncertainties is interesting in this context: Epistemic: uncertainty related to a lack of knowledge or information in any phase or activity of the design process [START_REF] Ganascia | Les sciences cognitives[END_REF].

Consequently, in order to improve Computer Aided Design Software (CAD, PDM and PLM essentially), the following question addressed in this paper, is then: "How to model product information and uncertainties in collaborative preliminary design?"

To answer the question a state of the art is built on uncertainty modeling and product/knowledge models to analyze how the product models support decision making taking into account uncertainties.

Literature survey

Table 1 is a synthesis of different qualitative and quantitative approaches allowing to qualify and quantify data uncertainty and to answer the questions identified in Section 3.1. The keypoints such as sustainability, sensitivity or collaborative dimension are presented in more detail in the following paragraphs. The product and knowledge models identified allow us to decompose, structure and take into account the different design activities of mechanical systems in order to support the product lifecycle management. However, it should be underlined that none of them considered uncertainties.

Table 1. State of art of the approaches

Uncertainties modeling

Product and knowledge models Qualitative approaches Quantitative app.

Sustainability [START_REF] Thunnisen | Propagating and Mitigating Uncertainty in the Design of Complex Multidisciplinary Systems[END_REF] Variation [START_REF] Gaudin | Description and modeling of information flow within an integrated design team, to define a tool of help for designer[END_REF] Sensitivity [START_REF] Grebici | Conflict management in design process: focus on change impact[END_REF] Completeness [START_REF] Krishnan | Managing the simultaneous execution of coupled phases in concurrent product development[END_REF] PEPS:

Precision, Accurancy, Parsimony, Specialisation [START_REF] Yassine | Engineering design management: an information structure approach[END_REF] Probability theory Fuzzy sets [START_REF] Sebastian | Knowledge modeling in mechanical embodiment design for real time simulation and decision support[END_REF] Possibility theory [START_REF] Zadeh | Fuzzy Sets[END_REF] [19] Evidence theory [START_REF] Du | A New Fuzzy Analysis Method for Possibility-Based Design Optimization[END_REF] [21]

PPO: Product Process Organisation [START_REF] Shafer | A Mathematical Theory of Evidence[END_REF] KCM: Knowledge Configuration [START_REF] Roucoules | The PPO design model with respect to digital enterprise technologies among product life cycle[END_REF] CPM: Core Product Model [START_REF] Badin | Using the Knowledge Configuration Model (KCModel) to manage configurated knowledge for upstream phases of the design Process[END_REF] MOKA: Methods and tools Oriented to Knowledge Acquisition [START_REF] Sudarsan | A product information modelling framework for product lifecycle management[END_REF] Qualitative approaches are based on the preliminary information concept introduced by Clark and Fujimoto [START_REF]deliverable du consortium MOKA[END_REF] to allow the parallel execution of activities in the product development processes. Eppinger [START_REF] Kim | Product Development Performance'. Strategy, Organization, and Management in the World Auto Industry[END_REF] defined the concept of preliminary information as a parameter that is in continual evolution before it achieves its final value. The status of the parameter in its evolution refers to its maturity [START_REF] Eppinger | A model-based framework to overlap product development activities[END_REF].

The qualification and characterization of the model and information include several aspects: sustainability, variation, sensitivity and completeness. Information within a design office can be classified with respect to the level of sustainability [START_REF] Thunnisen | Propagating and Mitigating Uncertainty in the Design of Complex Multidisciplinary Systems[END_REF] that is to say, the longevity of the information. A scale from "1" (Information not sustainable) to "5" (valid information for the currently used technologies) is used and refers to the information validity degree.

Sensitivity levels define the impact of change on information, according to [START_REF] Grebici | Conflict management in design process: focus on change impact[END_REF] are classified along a scale from "0" corresponding to not sensitive, to "3" corresponding to sensitive.

Generally, three main categories of knowledge are distinguished in a development process: product engineering knowledge, manufacturing process knowledge and organizational knowledge. Another kind of knowledge concerns the capitalization of decision justification during the development project.

In the literature, several recent works exist, dealing with models in order to represent product, process and organization knowledge. These works are principally developed in three scientific fields: development of domain ontology in order to identify the mains concepts of a domain and the relationships between these concepts [START_REF] Hanssen | Reducing delivery times in engineer-to-order firms by using the concepts of concurrent engineering[END_REF]; the development of projects memory that aims at achieving the traceability of the project evolution for reuse perspective [START_REF] Uschold | The Enterprise Ontology[END_REF] and finally, the development of business tools such as PDM and CAX tools in order to support the technical activities of designers [START_REF]deliverable du consortium MOKA[END_REF].

The commonly accepted approach for structuring product knowledge has been through the construction of Product Models. As an example of such models, [START_REF] Matta | Méthodes de capitalisation de mémoire de projet[END_REF] translated NIST's core product model [START_REF] Badin | Using the Knowledge Configuration Model (KCModel) to manage configurated knowledge for upstream phases of the design Process[END_REF] and proposed an ontology for the Open Assembly Model (OAM) implementing several OWL (Ontology Web Language) capabilities. Lee [START_REF] Fiorentini | Towards an Ontology for Open Assembly model[END_REF] has developed a model for sharing product knowledge of the Beginning Of Life (BOL) on the web. Terzi [START_REF] Lee | OWL-based product ontology architecture and representation for sharing product knowledge on a web[END_REF] has proposed to use the concept of Holon for the description of product knowledge. The Holon is defined as a composition of a physical entity and all of its related information.

In parallel, the process knowledge definition is based on activity models: activities allow creation of the link between products, resources (facilities, humans...) and their characteristics (behavior, task, properties…), they structure and define the behaviour of the processes. An activity aggregates several kinds of knowledge such as sequences, functions, rules, states [START_REF] Terzi | Development of a meta-model to foster interoperability along the product lifecycle traceability[END_REF]. It concerns the process scheduling, the set of resources (human resources, machines, tools and tooling), the organization of the production unit (work centres) and the manufacturing know-how [START_REF] Hugo | The Structuring of Process Knowledge: Function, Task, Properties and State[END_REF].

Other categories of models are developed with generic perspective in order to cover heterogeneous knowledge fields [START_REF] Sudarsan | A product information modelling framework for product lifecycle management[END_REF]. For instance, Nowak [START_REF] Hugo | The Structuring of Process Knowledge: Function, Task, Properties and State[END_REF] have presented the architecture of a collaborative aided design framework integrating Product, Process and Organization (PPO) models for engineering improvement. The PPO information kernel stores persistent data on the interoperable files that might be reached by several external applications on the collaborative PLM system among the whole product life cycle [START_REF] Shafer | A Mathematical Theory of Evidence[END_REF]. Danesi [START_REF] Fortin | Manufacturing Process Management: iterative synchronisation of engineering data with manufacturing realities[END_REF] have proposed the P4LM methodology, which allows the management of Projects, Products, Processes, and Proceeds in collaborative design. It aims at allowing the integration of information coming from different partners which are involved in a PLM application. The KCM (Knowledge Configuration Model) is another example of knowledge model, which is developed with the aim to manage knowledge using configurations synchronized with expert models that enable designers to use parameters consistently in a collaborative design process [START_REF] Roucoules | The PPO design model with respect to digital enterprise technologies among product life cycle[END_REF]. The KCM approach is based on the concept of "knowledge configuration", which is a virtual object composed by a set of parameters and rules instantiated from the generic baseline and contextualized into an expert model for a specific milestone of the project in order to ensure consistency and decision making supported by all expert knowledge.

All of these models allow us to represent product or knowledge and ensure the data consistency but no one of them take in consideration uncertainties and maturity of data and mechanical systems, in order to help the decision making.

A meta-model for knowledge representation integrating maturity 4.1 The key factors and the metric allowing to define maturity

The presented metric allows us to evaluate the maturity of a mechanical system by calculating the maturity of each components to each iteration of design. The equation ( 1) presents how the maturity of a component (Ci) is defined, where 'i' is the number associated to the component. The metric evolves with each design iteration, and as a consequence each parameter is constantly updated until it meets the full technical specification of the need. The factors are "n", "value", "tolerance", "SusSen", "Perf" and "Coi".  "n" is the number of design parameters of a part (diameter, length, …)  "value" is the nominal value of the design parameter, (diameter=25mm).  "tolerance" is the domain of variation of the value, (diameter=25 ±5mm).  "SusSen" represents the association of Sensitivity and Sensibility of the information. A first designer which has created this information (design parameter and tolerance) characterizes it using a sustainability level based on qualitative scale like described by Gaudin [START_REF] Thunnisen | Propagating and Mitigating Uncertainty in the Design of Complex Multidisciplinary Systems[END_REF]. The level of sustainability is the time during which information may be considered as valid. The level of sensitivity is the impact importance of the data on the assembly. The designer qualifies the result due to a sensitivity level based on qualitative scale like described by Krishnan [START_REF] Grebici | Conflict management in design process: focus on change impact[END_REF].  "Perf" is the level of performance is defined by the percent of requirement number achieved by the end of the design iteration in comparison with the number of total requirements of the part in question. For example, if a part has three requirements and only two are achieved by the end of the design iteration, then the level of performance for this part is 66%. When 100% is achieved it means that all technical specifications of the need are completed.  "Coi" is the level of maturity that we wish to achieve at the end of the design iteration. This is a constant that allows the adjustment of the level of maturity.

Methodology to use the metric

The result of the metric (level of maturity) is actualized at the end of each design iteration in order to help the decision making for the next design iteration.

The first step to build and use the metric is done by the first designer by defining the design parameters in CAD software such as CREO ® or CATIA ® More than the nominal value of the parameter, he is defining the interval of possible values ("tolerance") and the level of sustainability based on qualitative scale like the one described by Gaudin [START_REF] Thunnisen | Propagating and Mitigating Uncertainty in the Design of Complex Multidisciplinary Systems[END_REF].

The part (with parameters, values, tolerances and level of sustainability) is integrated in a PDM system, as metadata, in order to capitalize knowledge. This will also allow us to share the information and to trace the previous information in the next design iteration.

The second point of the methodology is the definition of the level of performance for the different parts composing the system.

The third step of the proposed methodology is the simulation of the assembly behavior of parts comprising the system. The simulation of the assembly behavior allows its approval. This study is done using simulation software such as NASTRAN, SIMULIA, etc. The designer does not only simulate the behavior of the assembly but does three points:

 Adjusts the tolerances using the results of the simulation.  Checks if the requirements are met.  Defines the level of sensitivity of the results of calculation (design parameters including tolerances). The level of sensitivity is the impact importance of the data on the assembly. The designer is able to qualify this result using a sensitivity level based on qualitative scale like described by Krishnan [START_REF] Grebici | Conflict management in design process: focus on change impact[END_REF].

At this step, all necessary factors are defined to calculate the level of system maturity. These factors are levels of sensitivity and sustainability of information, importance of tolerances in function of the value and the level of performance. The maturity is translated as a percent of the association of these three factors taking into consideration the goals to achieve, the user experience and knowledge, and the precision of the tolerances.

This metric helps the decision making for the next design iteration by highlighting the parameters where the unknown is the most important. For example, designer could have devoted more effort to a design parameter with a low level of sustainability and a high sensitivity instead of focusing on a parameter having a high level of sustainability and lower level of sensitivity; this way it may be easier to make decision between different point of views and design activities.

Proposed meta-models and models integrating maturity

The goal of the proposed meta-models is to provide a tool able to federate data, ensure consistency and integrate maturity in order to help to the decision making. The Data Meta-Model (DMM) generates a Data Model (DM) and the Collaboration Meta-Model (CMM) generates a Collaboration Model (CM). These Meta-Models are instances of the so-called Knowledge Meta-Model (KMM). They are described as follows:

Data's meta-modeling: the Data Meta-Model (DMM) puts the concepts allowing the representation of the business knowledge within a common and simplified semantic. In particular, it includes the parameters, their relationships and the maturity information.  RoleRelation class manages the relations, namely to give a direction to the relation, to handle the spread of the modifications using a tree approach instead of CSP.

Conclusion

In the product development project, the large variety of knowledge models identified in the literature survey pointed out the importance of robust meta-modelling approach to guarantee the coherence of the elements of heterogeneous knowledge produced during the collaborative design project. Indeed, this knowledge is generally coming from various sources, expressed with different semantics and supported in large numbers of business models.

Based on the analysis of the literature survey and the industrial experience, we have proposed in this paper a new meta-modelling approach integrating maturity that aims to help take into account the lack of knowledge (uncertainties and maturity) in decision making during preliminary design in a collaborative environment, but also to support the integration of multi-knowledge models and guarantee data consistency.

Current work consists to validate this proposal by doing an implementation of the MMK in the KCM (Knowledge Configuration Model) and by this way to validate its feasibility. Another level of validation also being established is to implement the MMK on a use case defines in partnerships with industrials in the case of the national project ADN.

Fig 1 .

 1 Fig 1. DMM package description Figure 1 details how the DMM package works. The content of the Entity class will be described later. The Data and Information classes inherit from the Entity class. For a given context of use, parameters and their values are enclosed in the Data class. MaturityFactors class composes Data class and allow the definition of the level of maturity (Maturity class). Performance class allows us to determine the level of performance based upon the SpecValidity relation. The Information class defines the knowledge configuration structure and the level of Maturity. Collaboration meta-modelling: the Collaboration Meta-Model (CMM) (Figure 2) proposes the concepts representing the collaboration between business models in the sense of flipping from one to another, and the Specification Model. This includes inter-business parametric relationships and model transformations. The Constraint class holds the business rules. The Transformation class outlines the transformation rules, that is to say the identification elements of equivalence relationships. The SpecValidity class checks the validation of the necessary technical specifications.

Fig 2 .

 2 Fig 2. CMM package descriptionKnowledge meta-modelling: the Knowledge Meta-Model, named KMM, is a conceptual framework allowing the creation of Knowledge Models (KM) through instantiation of the KMM. This way, the collaboration between KMM is supported. As pointed out in the previous section, there are numerous Knowledge models. Therefore, the KMM must be user-friendly and generic for the purpose of bringing consistency within one conceptual representation in order to open the possibility of combining different models and then building the most appropriate one.The MMCore package (Meta Model Core) is the heart of the modelling approach. It contains all generic classes that are common for the different meta-models. The

Fig 3 .

 3 Fig 3. MMCore package description Figure 3 presents the UML diagram of the MMCore package. The MMCore includes six main classes:  The Element class is the most generic level of the MMKM.  XModel class defines the type of model (data, collaboration) linked to an element  Description class enables the formulation of a specification as any of the quantifiable properties  Entity class capitalizes and structures on the main data extracted from business models or from experts.  Relation class provides a link between the components of the Entity class. RoleRelation class manages the relations, namely to give a direction to the relation, to handle the spread of the modifications using a tree approach instead of CSP.

Acknowledgement: This work has been realized in partnership with the project A.D.N. (Alliance des Données Numériques) supported by French public funds through the FUI9 program.


Guillaume Herlem

Guillaume Ducellier

Pierre-Antoine Adragna

Alexandre Durupt

Sébastien Remy

A reverse engineering method for DMU maturity management: use of a functional Reeb graph

Keywords: DMU, PLM, Reverse Engineering, Reeb Graph, CAD, Shape Matching

In a development process, Digital Mock-Up (DMU) is the referential view of the developed product, all along the product states ("as-design", "as-manufactured", etc.). In case of long lifetime products, such as boats or planes, each released product has its own DMU. During the use, maintenance operations are made. Some of the modifications are made "on field" and are not always reported to the DMU which is no longer mature. The product lifecycle is impacted and not efficient anymore. This paper focuses on a systematized methodology for checking the maturity level of a DMU by confronting it to the real product it is intended to represent. DMU inconsistencies correspond to unreported product components modifications. The first step of the process is a global comparison between the DMU and a digitization of the in-use product using the Reeb graph. The used comparison criterion is the topology. The second step is to identify the kept DMU components in the digitized dataset and thus deduce inconsistencies. That step implies a new shape descriptor combining topological and functional descriptions of a mechanical part: the Functional Reeb Graph.

Introduction

In the industry, PLM (Product Lifecycle Management) solutions are widely used for supporting product development projects. They are intended to be the answer to collaborative design and integrated engineering. It implies many actors with various kinds of expertise and often located in different geographical places. The modern way to develop a product then is in extended mode: partners share and manage design information with massive use of 3D models. The project relies on a referential representation: the DMU (Digital Mock-Up) which is basically a representation of the product BOM (Bill of Materials) by an assembly of CAD models. It gathers different views of the product development related to expertise domains. The DMU is intended to be a referential representation during the different states of the product development: "as-design", "as-manufactured", "as-used", etc. For mass consuming products (few years), a common DMU is used for all produced products. But for long lifetime products (dozens of years) such as aeronautical ones for example, each produced product has its own DMU. Even if the design or manufacturing phases are well handled, the "as-used" state can be more uncertain. In fact, unplanned maintenance activities can be made "on-field" on a long lifetime product, generating variations that are not always reported to the DMU. The PLM is no longer efficient. The inconsistency of the DMU is a problem which impacts further planned modifications: design activities based on non-up-to-date DMU data generates mistakes and additional delays caused by an a posteriori on-field data discrepancy checking. This paper focuses on a reverse engineering approach for identifying DMU inconsistencies in a discrepancy checking context. We propose to acquire a virtual view of the in-use product and confront it to its digital representation. We present a new way to describe CAD models for identifying components in a digitized product assembly. The aim is to isolate the implied component(s) causing the maturity differential with the in-use product. The process is intended to be integrated in a CAD environment. In section 2, we briefly present related works. The proposed solution is presented in section 3. In section 4, we discuss on our solution.

Related Works

Our problematic is partially covered by 3 scientific research domains: (1) the global aim is to check the DMU and its level of maturity, an activity close to "discrepancy checking", (2) our approach of the problem (retrieving virtual data from a real object) is similar to "reverse engineering" approaches and (3) identifying 3D models (identifying components) in a scanned dataset is linked to "shape matching" studies.

DMU Discrepancy Checking

DMU discrepancy checking is a kind of 3D inspection which consists in con-fronting a manufactured model to its digital representation. The purposes can be multiple: checking unplanned component and validate the manufacturing as in [START_REF] Georgel | Navigation tools for viewing augmented CAD models[END_REF] and [START_REF] Kahn | 3D discrepancy check via augmented reality[END_REF] or update the design after manufacturing a physical prototype as in [START_REF] Webel | Identifying differences between CAD and physical mock-ups using AR[END_REF]. 3D inspection is well-covered and widely used in the industry to check mechanical parts. A 3D model is retrieved by generating surfaces on a digitized point cloud (using laser scanner for example) from a real model. Then, the 3D inspection resides in the analysis of the deviation between the original design and the captured one. DMU discrepancy checking is the application to assembly models. The recent works use other means than laser scanner like augmented reality ( [START_REF] Kahn | 3D discrepancy check via augmented reality[END_REF], [START_REF] Webel | Identifying differences between CAD and physical mock-ups using AR[END_REF]) or on-site pictures ( [START_REF] Georgel | Navigation tools for viewing augmented CAD models[END_REF]). The proposed solutions are applied to static systems like plants or piping. There is not any application on mechanical systems (with mobility and different poses between the DMU and the reality).

Reverse Engineering

Retrieving data from a real part or a product into a virtual engineering framework is known as Reverse Engineering (RE). The main objective is to enable redesign or new design activities based on a physical object. In the scientific literature, RE is a well-covered domain. The input data is generally a point cloud (acquired as in 3D inspection for instance). The first studies, initiated with [START_REF] Várady | Reverse engineering of geometric models -An introduction[END_REF], were based on a pure geometrical approach with the fitting and assembling of surfaces on the point cloud. The rebuild model is automatically generated from the point cloud and that fullygeometrical approach is well-implemented in commercial software. But the model is generally frozen and does not enable redesign activity without tedious work. Another kind of approach exists: rebuilding a model using features. Studies like [START_REF] Durupt | KBRE: A knowledge based reverse engineering for mechanical components[END_REF] propose to retrieve a 3D model by fitting manufacturing features or functional features in the point cloud. The features are geometrical sets driven by parameters. The result is close to a common 3D model handled in CAD software. Thus, redesign activity is possible. For now, all those solutions have been fully-tested on single part component. A new scientific and industrial challenge is to apply them to the components assembly context in order to get a "virtual view" of an in-use product assembly.

Shape Matching

Comparing two 3D models is covered in the "shape matching" (or "shape retrieval", "shape indexing") methods. It consists in defining a (dis)similarity level between the models by comparing them using their shape signature. The most common purpose is to retrieve similar shapes in a 3D models database. And the main challenge of shape matching is to describe in a compact but highly descriptive way the shape of a 3D model. That shape signature is obtained with the use of a shape descriptor, mainly applied on the 3D model meshed surface. A shape descriptor is a 3D model discretization based on an abstraction of the model. A way to check the discrepancy between two products assembly representations is to identify common components between them. Shape descriptors have that potential possibility which has not been tested yet.

Among the different shape descriptors in the scientific literature, the Reeb Graph (RG) initiated in [START_REF] Reeb | Sur les points singuliers d'une forme de Pfaff complètement intégrable ou d'une fonction numérique[END_REF], is a well-covered graph-based representation with a high descriptive power and a high potential of evolution, as in [START_REF] Hilaga | Topology matching for fully automatic similarity estimation of 3D shapes[END_REF], [START_REF] Tung | The augmented multiresolution Reeb graph approach for contentbased retrieval of 3D shapes[END_REF] and [START_REF] Tierny | Partial 3D shape retrieval by Reeb pattern unfolding[END_REF]. The generation principle is simple 3 . A scalar function is applied on each vertex of the meshed model. The vertices values are sorted into groups of same values intervals. A RG node is attributed to each group of vertices (the node is the barycenter of the mesh portion) and a RG edge connects two RG nodes if the corresponding mesh portions are connected. The RG nodes gather attributes: computed local information like surface area or curvature. The 3D models comparison is then simplified to a 1D graphs comparison. The choice of the mathematical function enables the RG to be insensible to the object orientation and pose. That property is useful four our context. Indeed, an in-use product may have a different pose and a different orientation (due to its installation, its use or its wear for example) from the setup defined in its DMU which can be arbitrary.

Conclusion on Related Works

In our study of the related works, we identify a shape descriptor with a high potential of application for our purpose: (1) comparing a DMU and a digitized dataset from a real product, (2) identifying DMU components in the dataset. For the first step, it can be simplified as the comparison of two 3D models, as in shape matching methods. It is possible to extract an external envelop from a DMU and mesh it. And a digitized point cloud from a real object can be easily meshed too. The comparison between two meshes is well-covered. The Reeb graph (RG) (and its extensions) is a powerful tool, suitable for the meshed assemblies' comparison. For the second step, it appears that topology (and local embedded geometrical information) is not enough. We need to retrieve components in a meshed assembly model. In terms of shape matching, it is similar to partial shape matching: the component model is here 3 More details on the RG generation are available in [START_REF] Hilaga | Topology matching for fully automatic similarity estimation of 3D shapes[END_REF].

Fig. 1. This paper proposition of a reverse engineering process for DMU maturity management a subpart of the assembly model. And even if it can be applied to simple shapes, mechanical parts are more complex: as stated in [START_REF] Herlem | DMU Maturity Management as an Extension of the Core Product Model[END_REF], the RG of a meshed components assembly model is not the "assembly" of the RG of each component model. The contacts between part components generate undefined zones.

Proposed Solution

Main Principle and DMU Inconsistencies Definition

We consider two input data:  A point cloud digitized from the real product  A product DMU at an unknown maturity state For being compared on a common criterion, the shape, the two inputs are meshed giving two 3D representations of the product. We propose a process in two steps assisted by a product expert:

1. Determining the presence of at least one inconsistency, 2. If at least one inconsistency, identifying the component(s) generating that inconsistency(ies)

That steps are made of activities, numbered 1 to 4 for the first step and numbered 5 to 8 for the second step (Fig. 1).

Our proposition is, at first, to consider a product assembly as a unique 3D model. So a component modification results in a modification of the topology of the assembly model. Our contribution is to use the study of the topology variation as an indicator of the maturity state of the DMU (among other possible indicators but not presented in this paper). Thus, an inconsistency is assumed to be:

A component which is in the real product compared to the DMU.

And then, for the first step, in a global approach, we propose to use the aMRG comparison presented in [START_REF] Tung | The augmented multiresolution Reeb graph approach for contentbased retrieval of 3D shapes[END_REF]. Our paper does not detail that first step, as we treat the meshed DMU and the meshed scanned data as two simple 3D models. The graphs generation and comparison are the same as the aMRG solution. In this section, we consider that the process is applied to an in-use product which has been modified and its DMU has not been updated. That suspicion has been confirmed to the expert by the first step of the process (the global comparison using aMRG).

added lacking replaced moved

In this contribution, we present the second step of our process: the recognition of the DMU components in the meshed scan (Activities 5 to 7). The expert focuses on the meshed scan area generating the dissimilarity score.

Our process is supported by a CPM (Core Product Model) extension originally introduced in [START_REF] Herlem | DMU Maturity Management as an Extension of the Core Product Model[END_REF]. The extension is made for supporting our approach by managing the new product representation: abstractions based on topology and kinematics and used in the descriptor presented in the next section. This paper does not present the data model.

A New CAD Model Descriptor

As stated in section 2.4, the RG is not suitable "as-is" for identifying a component in a meshed assembly. For supporting our identification purpose, we propose to derivate a new RG shape descriptor (more specific to CAD models) from a simple definition of a mechanical part:

"Functional features linked by Topological features"

In our study, we choose to restraint functional features to be the sets of geometrical elements of a part aimed to enable mobility with compliant features of another part. We only consider the functional surfaces linked to the kinematic domain. The other geometrical elements are simply considered as links between functional features, as Fig. 2. The FRG generation on a lower triangle of a suspension topological features. This descriptor, the FRG (Functional Reeb Graph), is a direct illustration of that basic definition. We extend the current works on the RG by a knowledge addition: the creation of a new graph node, the functional node. That node is aimed to describe the "undefined zones" detailed in [START_REF] Herlem | DMU Maturity Management as an Extension of the Core Product Model[END_REF]. In fact, in the case of a mechanical product, they are the areas corresponding to the kinematic pairs of the product. We proposed to inspire the new descriptor generation from [START_REF] Tierny | Partial 3D shape retrieval by Reeb pattern unfolding[END_REF]: using feature points as sources for the RG generation. In our case, the feature points are the centroids of the functional zones of a part, the functional nodes. The principle then is to link the different functional zones by travelling along the topology of the model. For that step, we use the approach of the geodesic distances computation presented in [START_REF] Hilaga | Topology matching for fully automatic similarity estimation of 3D shapes[END_REF] and [START_REF] Tung | The augmented multiresolution Reeb graph approach for contentbased retrieval of 3D shapes[END_REF]. An example of the generation of an FRG on mechanical part is displayed in Fig. 2.

The computation of the FRG follows this procedure:

1. For each functional node (from functional zones, Fig. 2a), the geodesic distances to all the mesh vertices are computed 2. Each mesh vertex is attached to the closer functional node, giving a set of "under influence" vertices for all functional node 3. For each set of vertices:

3.1. The vertices are sorted by group of geodesic distance intervals (Fig. 2b) 3.2. A topological node is attributed to each group of vertices (as in [START_REF] Hilaga | Topology matching for fully automatic similarity estimation of 3D shapes[END_REF] and [START_REF] Tung | The augmented multiresolution Reeb graph approach for contentbased retrieval of 3D shapes[END_REF]) 3.

3. An edge links two nodes if the vertices groups are connected (as in [START_REF] Hilaga | Topology matching for fully automatic similarity estimation of 3D shapes[END_REF] and [START_REF] Tung | The augmented multiresolution Reeb graph approach for contentbased retrieval of 3D shapes[END_REF]) 4. The connected sets are linked by merging the corresponding leaf nodes (Fig. 2c)

The Inconsistency Identification

This section presents the aim of the FRG: enabling the inconsistency identification (activities 5 to 8 in Fig. 1).

Functional Description (5) Using a custom user interface, the expert defines the kinematic pairs locations on his focus on the meshed scanned product. He uses enclosing spheres that can be stretched (Fig. 3). Not all the system is described. Our use case in Fig. 4 is a mechanical part of a vehicle suspension. The previous step of the process enables to locate the dissimilarity area so only that area is treated, not all the vehicle or all the chassis. From this enclosing shapes (Fig. 4a), which are the functional zones (darker mesh areas in Fig. 4b), the centroids are computed giving the functional nodes.

Topological Description (6) This activity corresponds to the FRG generation: the topological links between the functional zones are computed (Fig. 4b), generating topological nodes and edges of the FRG. The same generation as described in 3.2 is applied to the meshed assembly. The resulting FRG then is a direct assembly of the FRG of the components as they are based on the functional zones which are undefined zones with previous RG alternatives.

Components Recognition (7) When the meshed scanned assembly have been described with the FRG, an algorithm is ran to identify the DMU components that are present in the real product scan. That algorithm is similar to partial shape matching basic procedures which can be found in the scientific literature. The variation is the addition of the number of functional nodes as a primary search criterion. For that activity, we assumed that all DMU components are described with the FRG and accessible by querying a database with our graph structure. And they are retrievable under a meshed form. A list of matching results is presented to the product expert who validates the right components. The Fig. 4c illustrates the recognized lower triangle in the meshed assembly scan. Using the nodes locations, each recognized and validated component mesh is coarsely aligned in the assembly mesh and then finely fitted using best-fit algorithms. The equivalent meshed scanned portion is then suppressed (Fig. 5).

Skeleton Extraction (8)

All the mesh areas of the identified components have been suppressed. The remaining mesh portion(s) (Fig 5) correspond to unrecognized component(s): DMU inconsistency(ies) as defined in section 3.1. The skeleton extraction from that remaining mesh is still a work in progress activity. Its aim is detailed in the further works in section 4.

Conclusion

This paper proposes an approach for checking DMU discrepancy. The main contribution is the development of a new CAD model descriptor based on the Reeb graph (and evolutions) shape descriptor: the Functional Reeb Graph. As a knowledge addition to the topological description, we proposed to take into account, in a basic way, the purpose of a mechanical part: the "kinematic" intent. The FRG allows identifying the inconsistencies of a product DMU as we defined them: unreported component modifications.

Discussion

The proposed process enable to isolate a partial meshed model corresponding to the unrecognized component which can be a useful basis for a reverse engineering process with a CAD commercial software. In the methodology setup resides two strong constraints: the studied system needs to be a kinematical one with a high exposition of its components and a product expert must be implied. Further Works The next step is to improve the information stored in the functional node. At the moment, it is only the centroid of a functional zone of a component. In the future, information on the kinematic pair the functional zone is intended to enable will be stored in the node. Further works will be on the information extraction from the isolated mesh part corresponding to the implied component. We strongly believe it is possible to store functional features and topological features directly into the nodes. The final stage of our approach will be the generation of a geometrical skeleton of the component enclosed in the remaining mesh portion of the scanned product. That skeleton will be a support for the redesign activity in a CAD environment for updating the DMU.

Fig. 4 .Fig. 3 .

 43 Fig. 4. The identification of the lower triangle in the meshed assembly scan using the FRG

Fig. 5 .

 5 Fig. 5. The suppression of the meshed scan portions corresponding to recognized components

Université de Technologie de Troyes, Troyes, France {guillaume.herlem, pierre_antoine.adragna, guillaume.ducellier, sebastien.remy}@utt.fr

Université de Technologie de Compiègne, Compiègne, France alexandre.durupt@utc.fr

Acknowledgments

The study presented in this paper is possible thanks to the European Regional Development Fund (ERDF) and the Champagne-Ardenne Region providing NUM3D equipment.