Wim J C Verhagen
email: w.j.c.verhagen@tudelft.nl

Pablo Bermell-Garcia

Pierre Mariot

Jean-Pierre Cotton

Domingo Ruiz

Romaric Redon

Ricky Curran

Ruiz

Pablo Bermell

Richard Curran

(Costing) Knowledge-Based Cost Modelling of Composite Wing Structures

Keywords: COMPOSITE MATERIALS, COSTING user): Engineering Knowledge Management engineering knowledge management, knowledge engineering, cost modelling, composites

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Introduction

A recent challenge in aerospace engineering is the trend to switch from predominantly aluminium to predominantly composite structures, as evidenced by the development of the Boeing B787 and the Airbus A350 XWB (Extra Wide Body). From the manufacturing perspective, this is a major step change since it imposes the need to create and update existing knowledge bases to adapt industry to this new demand. The UK government has recognized this issue in its national Technology Program. Consequently, a series of research initiatives are being funded to build the necessary industrial foundations and knowledge to face the composite manufacturing challenge (Airbus, 2010, Technology Strategy Board, 2009). this, the solution does not replace the natural use of spreadsheets to compute the cost of components. However, it manages the knowledge driving the cost model and deploys it to a working spreadsheet from which users can understand the rationale of the computed cost.

• Lifecycle management of the knowledge: The KM capability enables taking diverse paths to evolve the cost model. However, it also enables control of the actual standard way of costing parts. Furthermore, it enables storing, justifying and updating cost model knowledge elements and records previous versions of the cost model.

This short introduction of the research problem, objectives and solution elements is substantiated in the remainder of the paper. First, the research background is evaluated.

This includes a brief assessment of relevant theoretical aspects, which is followed by a more involved description of the research problem in terms of managing engineering knowledge and associated elements. Subsequently, an explorative case study is performed which analyses cost modelling of aerospace composite components through life. The research problem and the case study elements are combined to lead to a knowledge-based framework for managing cost models through life, which is discussed in Section 4. First, semantic unification of the cost model elements and knowledge base structure is achieved. Next, the framework principles and architecture are proposed.

Finally, a proof of concept application is described which illustrates the functional implementation of the framework principles and architecture. This proof of concept serves as an initial validation of the knowledge-based cost modelling approach. A final discussion with conclusions and limitations wraps up the paper. In this Section, literature is reviewed to establish the context of the research. First, current approaches to cost estimation are categorized, which allows the cost modelling approach used in this paper to be placed into context. Subsequently, the concept of knowledge is briefly explored, followed by its application in engineering knowledge management.

Next, three major KM principles are explored. This serves to show which elements in KM are currently insufficiently addressed in theory and practice, and thereby guides the approach for the case study and the solution framework.

Categorization of current approaches to cost estimation

A number of authors discuss and categorize current approaches to cost estimation. The most universally adopted perspective on cost modelling [START_REF] Feldman | Model for cost estimation in a finite-capacity environment[END_REF][START_REF] Newnes | Predicting the whole-life cost of a product at the conceptual design stage[END_REF][START_REF] Curran | Review of aerospace engineering cost modelling: The genetic causal Approach[END_REF][START_REF] Price | An integrated systems engineering approach to aircraft design[END_REF] refers to three approaches, namely analogous, parametric and bottom-up cost modelling. [START_REF] Curran | Review of aerospace engineering cost modelling: The genetic causal Approach[END_REF] present a matrix of comparative assessment for these methods, which is given here in Table 1. It is important to note here that one of the subsets of bottom-up modelling is physical process modelling, which focuses on the time required to carry out work [START_REF] Curran | Review of aerospace engineering cost modelling: The genetic causal Approach[END_REF] as this principle is used in the cost modelling approach outlined in this paper (see Sections 3.1 and 4.1.1).

Table 1: Assessment matrix for traditional cost estimation methods [START_REF] Curran | Review of aerospace engineering cost modelling: The genetic causal Approach[END_REF]) [START_REF] Curran | Review of aerospace engineering cost modelling: The genetic causal Approach[END_REF] also distinguish between 'traditional' and 'advanced' estimating approaches. The three approaches mentioned in et al. (2004) also introduce the genetic causal cost model to address the need for a more scientifically based methodology for cost estimation. [START_REF] Newnes | Predicting the whole-life cost of a product at the conceptual design stage[END_REF] add a more upper-level categorization perspective by considering cost estimation approaches as being 'generative' or 'parametric'. In the generative process, the cost estimation builds upon the data that is gathered during the design process. Consequently, the costing estimate accuracy depends on the level of data detail. In parametric approaches, estimates are 'achieved based on past experience, using findings from past products and estimating the expected cost' [START_REF] Newnes | Predicting the whole-life cost of a product at the conceptual design stage[END_REF].

Parametric approaches distinguish themselves by the use of cost-estimating relationships (CER). Newnes et al.'s (2008) categorization relates closely to the level of fidelity of the cost estimation approaches; as [START_REF] Price | An integrated systems engineering approach to aircraft design[END_REF] indicate, 'analysis fidelity relates to the degree of detail and accuracy contained in a given analysis model'. [START_REF] Price | An integrated systems engineering approach to aircraft design[END_REF] distinguish three levels of fidelity. Low fidelity models use simple equations and look-up tables, and frequently do not have associations with geometric models. Medium fidelity models use some form of linear analysis in combination with geometric model information and high fidelity models contain a lot of detail while modelling non-linear behaviour. When looking at the issue of fidelity from a more multidisciplinary perspective, a number of research gaps can be identified (adapted from [START_REF] Price | An integrated systems engineering approach to aircraft design[END_REF] -see Table 2. It is interesting to note that significant gaps exist for the cost discipline (at the medium to high fidelity levels) and manufacturing (at the low to medium fidelity levels). This conclusion will be revisited when inspecting the cost modelling approach outlined in this paper (see Section 3.1).

2.2

Engineering knowledge management [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF] defines knowledge as 'a justified belief that increases an entity's capacity for effective action'. [START_REF] Alavi | Review: Knowledge management and knowledge management systems: conceptual foundations and research issues[END_REF] offer a deep and involved literature review on the concept of knowledge; in particular, their main conclusions are that 1) literature focuses heavily on distinguishing between data, information and knowledge, 2)

knowledge is personalized and should therefore be expressed in such a manner that it is interpretable to receivers, and 3) only information that is actively processed in the mind of an individual or individual(s) is useful. These realisations are carried through in this paper; in its applied form (see Sections 3 and 4) 'knowledge' covers the engineering domain and encompasses parameters and their values as well as formulas (together constituting data and information), as well as the justification for these parameters and formulas (constituting the conventional 'knowledge' as used in literature). Furthermore, the realisations that knowledge must be interpretable to receivers and must be processed by users to have an impact are guiding the solution development effort (Section 4).

This solution effort focuses on the application of knowledge management to support an engineering task. Knowledge management can be defined as 'a systemic, organized, explicit and deliberate ongoing process of creating, disseminating, applying, renewing and updating the knowledge for achieving organizational objectives' (Ammar- [START_REF] Ammar-Khodja | An overview on knowledge management[END_REF]. Knowledge management helps organizations in various ways, such as retaining knowledge after loss of key staff and ensuring effective use of structured knowledge, which enables quicker identification, retrieval and leverage of existing company knowledge [START_REF] Alavi | Review: Knowledge management and knowledge management systems: conceptual foundations and research issues[END_REF].

For the engineering domain, the need to effectively use structured knowledge to improve engineering work has attracted considerable research effort in the last decades [START_REF] Oldham | MOKA -A methodology for developing KBE applications[END_REF][START_REF] Stokes | Managing Engineering Knowledge -MOKA: Methodology for Knowledge Based Engineering Applications[END_REF][START_REF] Curran | Design Integration of Multidisciplinary Engineering Knowledge within a Knowledge-Based Engineering Framework: the KNOMAD Methodology[END_REF]. For instance, the MOKA project (Methodology and tools Oriented to Knowledge-Based engineering Applications) [START_REF] Oldham | MOKA -A methodology for developing KBE applications[END_REF][START_REF] Stokes | Managing Engineering Knowledge -MOKA: Methodology for Knowledge Based Engineering Applications[END_REF] has been a key research effort focused in applying Knowledge Engineering principles in the engineering design domain. It focused on the conversion of tacit knowledge to explicit knowledge through the application of informal and formal models. However, it has become apparent that having an approach to make knowledge explicit is a necessary but insufficient condition to support knowledge intensive work. [START_REF] Siemieniuch | Organizational aspects of knowledge lifecycle management in manufacturing[END_REF]

Managing engineering knowledge: exploitation, visibility and lifecycle

The previous discussion indicated a number of aspects that together constitute the fundamental elements of the research problem from a theoretical perspective. First of all, the research challenge is on producing knowledge management capabilities that support the use of dynamic, evolving knowledge through-life. Furthermore, the knowledge contained in such capabilities must be interpretable to receivers and must be processed by users to have an impact. These elements are further addressed in this subsection under the notional headings of the operational exploitation of knowledge, the visibility of knowledge, and the knowledge life-cycle.

Historically, the exploitation of knowledge was at the forefront in the development of knowledge-based systems. Exploitation of knowledge often simply meant that organizational knowledge was incorporated into a KM application; more recently, there is an increasing recognition of the fact that KM applications should put any required knowledge as close as possible to the operational needs of the user (Mariot et al., 2007b). The operational exploitation of knowledge must be sufficiently addressed in knowledge management capabilities. As [START_REF] Redon | VIVACE Context Based Search Platform[END_REF] users. This is relatively easy to achieve for newcomers but more difficult for already experienced engineers, which is frequently the case at established industrial consortia.

For experienced engineers, only knowledge integrated with business applications can bring the required added value. This issue has been recognized for many years, but has not yet been adequately addressed. The aforementioned MOKA project [START_REF] Stokes | Managing Engineering Knowledge -MOKA: Methodology for Knowledge Based Engineering Applications[END_REF] also tried to solve this issue. MOKA exploited previous research efforts on Knowledge

Engineering by the adoption of the CommonKADS methodology and its principles [START_REF] Schreiber | Knowledge Engineering and Management: the CommonKADS methodology[END_REF] [START_REF] Preston | Knowledge acquisition for knowledge-based engineering systems[END_REF].

This ties in with the increasing realization in the knowledge research community that knowledge management applications should govern the whole knowledge life-cycle [START_REF] Ammar-Khodja | An overview on knowledge management[END_REF]Ammar-Khodja, 2008, Mariot et al., 2007a). The knowledge life-cycle is conventionally interpreted as the continuous, iterative transfer of tacit to explicit knowledge [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF] and vice versa. The previously mentioned MOKA project [START_REF] Stokes | Managing Engineering Knowledge -MOKA: Methodology for Knowledge Based Engineering Applications[END_REF] [START_REF] Sudarsan | A product information modeling framework for product lifecycle management[END_REF]. MOKA's methodological approach in its guise of interrelated informal and formal models addresses a key part of the knowledge lifecycle, namely the iterative conversion from tacit to explicit knowledge and vice versa.

However, MOKA does not fully address the many nuances of the knowledge life-cycle.

For example, an important part in managing the knowledge life-cycle is to ensure full traceability of knowledge and its source(s), as well as the accompanying enrichment process. Also, the traceability argument extends over the design process: throughout this process, concepts are created, trade-offs are performed, and decisions are taken. If the supporting knowledge for this process changes, the impact this has on the design process must be evaluated. Management of the knowledge life-cycle can valuably assist in such an evaluation. Unfortunately, theoretical support for these specific issues in the knowledge life-cycle and practical examples for the implementation of the knowledge life-cycle are limited. In this paper, this research gap will be addressed by covering the life-cycle from an applied perspective.

Costing aerospace composite components through life: a case study

In this section, a current business approach towards cost modelling of aerospace composite components through life will be studied. It will be shown that the evolution of the cost model and its use in the business process interrelate highly with the fundamental theoretical elements that need to be addressed for knowledge-based applications. Three fundamental elements of the research problem (knowledge exploitation, visibility and life-cycle) have been identified and put into a theoretical context. In this subsection, a specific business case will be evaluated that shows how these elements also form a challenge from a business perspective.

Cost model evolution

The business case concerns a bottom-up cost model that has been developed to address current issues on costing concept designs of composite parts and assemblies. In literature, research on cost modelling is rich and varied, both from a general perspective (as discussed in Section 2.1) and from more applied perspectives, for instance throughlife costing [START_REF] Curran | Influence of Manufacturing Tolerance on Aircraft Direct Operating Cost (DOC)[END_REF], Curran et al., 2007b, Curran et al., 2008a), manufacturing cost estimation (Curran et al., 2006a, Curran et al., 2008c) and composite material costing (Curran et al., 2008b). Likewise, a number of cost estimation methods and tools using principles established in literature have been developed for business use in aerospace Original Equipment Manufacturers, or OEMs. However, the aforementioned bottom-up cost model is filling a business gap that is not covered by other approaches within the aerospace OEM that cooperated in this research. In fact, the cost model addresses the medium level of fidelity gap that has been identified in section 2.1.

Furthermore, its results enable the parameterisation of the costs and times associated with the discrete manufacturing processes for composite parts. Going down to this level of detail in the model enables the generation of discrete event simulations in order to evaluate the initial proposed manufacturing system for meeting a required production rate. In other words, the cost model functionality also acts as an enabler towards lowfidelity estimation for the manufacturing discipline. The cost model evaluation enables the generation of improved manufacturing systems that respond better to the required rate under some specific criteria (cost, time, etc.). This results in a revised manufacturing system that meets production requirements against improved performance.

The research problem addressed in this paper emerged from the difficulties of coping with the complexity added to the cost model during its evolution. A snapshot of this process is illustrated in Figure 1. for different composite materials and material types. In this process, some of the knowledge gets classified and distributed to its consumers.

However, a significant risk of inconsistency and duplication of the information emerges.

Cost model research elements: knowledge exploitation, visibility, and life-cycle.

Following the analysis of the cost model, it is apparent that the evolution of this cost model has made it very difficult to manage its complexity, as the evolved model consists of many interrelated formulas and inputs that are insufficiently classified. An associated supporting informal knowledge is also very hard to find. Consequently, knowledge visibility is hardly achieved.

Finally, the assumptions, inputs, operations and outputs of the cost model are not managed from a life-cycle perspective. As mentioned, there are currently little to no provisions for explanation of the rationale behind assumptions. Furthermore, the model inputs, operations and outputs change during the lifetime of the cost model, but these changes are not stored, let alone tracked. The enrichment of these knowledge elements during the model life-cycle is not taken into account. This lack of knowledge life-cycle management is a significant stumble block in the learning process towards composite component production and associated cost estimation.

To summarize, three fundamental knowledge management research elements must be addressed from both a theoretical and a business perspective: the knowledge lifecycle, knowledge exploitation and knowledge visibility. The aim of this paper is to present a functional integration of a knowledge management environment and a real-life, operational cost modelling tool. The resulting knowledge-based cost modelling solution will provide both theoretical and practical relevance by addressing the aforementioned fundamental research elements.

A knowledge-based framework to manage cost models through life

In this Section, a knowledge-based framework to manage cost models through life is posited. The primary aspect of this framework is to tie in the cost model with an underlying knowledge base, which offers the opportunity to manage the model's complexity. Furthermore, it increases operational usability and attractiveness by

Semantic unification to enable knowledge-based cost modelling

In this Section, an analysis will be performed on one part of the full cost model focusing on carbon fibre-reinforced plastic (CFRP) wing top cover manufacturing.

Cost model entities

The cost model is spreadsheet-based and primarily process-oriented. For a given product with adjustable characteristics, it allows for the flexible estimation of time and cost associated with manufacturing options. The model can be classified as bottom-up, as it uses a physical process modelling approach to estimate costs. The model uses manufacturing processes and underlying sub-processes in combination with relevant manufacturing parameters to arrive at estimates for process times and costs, which are subsequently added to arrive at totals for cost and time. A simple example is shown in Table 3 for some steps in the cost modelling of T-stringer (a stiffening structural member) production, which is part of CFRP wing top cover manufacturing. This example shows the first steps in arriving at process time estimates for the individual sub-processes.

First, a set of parameters are derived from geometric models that are coupled with the cost model spreadsheets. These parameters are allocated per detailed process (as given in the left-most column under the 'Calculation' heading). The geometric parameters are then multiplied with or divided by applicable constants or rates, both in baseline (current performance) and target (required performance) forms. This results in time estimates for the individual detailed processes. This is followed by a considerable number of The previous analysis of the partial cost model can be schematically summarized into a use process with a number of generic elements, as illustrated in Figure 3. • Predefined formulas: These are formulas that use either assumed or user definitions to compute relevant values for each predefined manufacturing process defined in the spreadsheet.

• Predefined manufacturing processes: The manufacturing processes are compositions of formulas and references to assumed and user definitions.

Their end results are process time and cost estimates. Their explicit representation on the spreadsheet enables users to easily visualize the range of available processes and their influence on the final cost of the parts.

Cost model context

The previously identified basic cost model elements are difficult to introduce into a contextual model as individual parameters and formulas are used multiple times in the cost model. Thus, identification of a context by way of these basic elements may lead towards multiple, disparately used, knowledge elements. This would cause difficulties in the necessary modularization of the cost model (see Section 4.2). For example, using the contextual descriptor 'Number of stringers (N Stringers)' would already lead to five disparate detailed processes in the small example given in Table 3. Therefore, other more suitable semantic cost model concepts are necessary to constitute a contextual model.

Fortunately, the cost model itself has been organised to conform to a very basic classification after its evolution into several decomposed spreadsheets. This These semantic cost model elements form the context dimensions for a top-level contextual description model that can be used to structure knowledge in the knowledge management application (see Section 4). A contextual description model contains the semantics that can be used to specify the context in which the user, application or model operates [START_REF] Redon | VIVACE Context Based Search Platform[END_REF]. The context dimensions are 'classes or attributes that describe the context' [START_REF] Redon | VIVACE Context Based Search Platform[END_REF]. As mentioned, the cost model semantic

Framework principles and architecture

To move towards a successful knowledge-based framework for composite cost modelling that improves upon the current cost modelling approach and answers to the aspects of knowledge exploitation, visibility and life-cycle management, a number of principles have to be obeyed. These principles are briefly illustrated below. Three distinct steps are identified:

• Modularization & Classification: the cost model has been modularized

to allow for flexible assembly of unique cost models. To achieve this, the existing cost model has been reverse-engineered to define 'building blocks' that describe all the pre-defined inputs, user inputs and model formulas that belong to a sub-process (see also Figures 5 and6). The use of building blocks in this manner has the desired effect of achieving central management of the inputs and formulas, instead of having multiple the knowledge base must be shared and agreed upon before acceptance of knowledge base changes.

The solution principles have been used to devise an implementation architecture that is shown in Figure 5. • Knowledge Reuse Engine: this architectural element drives the queries that are used to retrieve building blocks from the knowledge base solution.

After selection, the building blocks are automatically exported to the spreadsheet environment.

• Knowledge Reuse Spreadsheet: the spreadsheet application retrieves the building blocks and automatically builds the resultant cost model, with all inputs, formulas and outputs in place. The cost model inherits default values from the knowledge base for the user definitions, but these and the other parameters can be changed to suit the user needs. An overview of the solution principles and its translation into a functional architecture is shown in Figure 6. Minor and major evolutions of the cost model (e.g. changes in formulas or changes in building blocks) can be performed by updating the relevant knowledge articles. Through the modularised approach, the changes are automatically incorporated into the building blocks and subsequently into any generated spreadsheet-based cost model.

Proof of concept

To establish the feasibility and validity of the framework, a proof-of-concept solution for knowledge-based composite cost modelling has been developed. This proof-of-concept is illustrated here by introducing the KM application and its use to implement the framework architecture, and by giving an instantiated solution example and the attendant user process.

The KM application of choice for managing the cost knowledge base and its elements is Ardans Knowledge Maker (Mariot et al., 2007a). This application offers a range of options designed to enable user-friendly management of knowledge, for instance through easy categorization and creation of knowledge articles. The application also an example is presented here that was used as part of a validation exercise at a major aerospace OEM. As presented in Section 4.1.1 previously, the example concerns the production of CFRP T-stringers for a wing top cover, for which the process representation was presented in Table 3. The contextual model described in Section 4.1.2 has been instantiated for this example; the result is shown in Figure 8. The contextual dimensions and their associated hierarchies -evidenced in Figure 8 for the product and process dimensions -have been implemented in AKM. These hierarchies can be partially seen in Figure 9. The user now follows the following process, which is also illustrated in Figure 10. The Figure shows that after opening a spreadsheet-integrated Visual Basic macro (step 1) and calling the AKM environment, the user employs the contextual information displayed in Figure 8 to select the relevant entries in the hierarchical trees (also shown in Figure 9).

The corresponding search result displays the suitable building block(s) (step 2). This building block is subsequently selected (step 3) and exported to the spreadsheet application. The supporting Visual Basic macro automatically generates the cost model (step 4) that belongs to the building block(s). After this, the user is free to make choices and changes in the spreadsheet environment to arrive at customised process time and cost estimates (step 5). The changes can be checked against the existing knowledge base as a 'live link' is maintained between the two applications. If necessary, the knowledge base can be manually updated to support knowledge evolution (step 6). Firstly, the solution to the case study provided knowledge life-cycle management through the inherit capabilities of the AKM tool and through the chosen solution methodology. In particular, the provisions for explicating the justification behind knowledge elements, the 'live link' between the knowledge base and the spreadsheet application, and the possibility to track the evolution of knowledge through the retention of historical knowledge articles contribute to a significant improvement on the life-cycle aspect.

Secondly, the solution also facilitated knowledge exploitation through the retention and exploitation of existing and legacy models, e.g. spreadsheet use, which ensured that users keep using familiar tools and processes. Furthermore, the use of a dedicated knowledge base, with the associated provisions to ensure the availability of trustworthy knowledge, assures users that the right knowledge is available at the right time.

Thirdly, the solution addressed knowledge visibility by including provision for knowledge explication. The difficulty with actually understanding complicated or dense legacy tools has been highlighted so that visibility is interpreted primarily in terms of the user being able to understand the rationale embedded into the tool. Users can adjust their confidence levels relative to the output results. Other features found to be of significance through the validation exercise included the traceability and retrievability of knowledge, potentially at the click of a mouse button. A framework and solution for knowledge-based cost modelling has been described in terms of its principles, methodology and architecture. Ultimately, a exemplar validation exercise was carried out. The developed solution delivers a key contribution from both a theoretical and practical perspective by enabling an integrated approach to performing process-based cost modelling supported by traceable, verifiable and reliable knowledge.

More specifically, this solution makes it possible to:

• Manage cost modelling knowledge bases in tandem with operational use.

• Manage model and legacy tool complexity.

• Manage risk associated with current business processes.

• Provide an easier, more user-friendly cost modelling capacity that can consequently be understood and used by more end users, enabling a better and more distributed capacity for cost modelling in early-stage design.

The In its current incarnation, the knowledge-based cost modelling solution is limited in several aspects. First, the whole process is at best semi-automatic. Composition of a cost model still requires some non-productive steps that don't add value, though the overall time spent on model composition is greatly reduced through the presented approach which produces something of great impact in a procedural and controllable manner.

Another limitation is the fact that the spreadsheet results from generated cost models and subsequent user manipulations are not yet stored into a central repository for the company; which is typical. The research has shown that it is advisable that these results are stored in such a repository for better understanding of the evolution of designs during the design process; this aspect will also be addressed in future research. A final limitation of the research that will addressed in future research is that the overall cost modelling capacity has not yet been fully addressed in informal terms: whereas the individual knowledge articles do feature informal knowledge, the higher-level process does not. This is a License Agreement between Wim Verhagen ("You") and Elsevier ("Elsevier") provided by Copyright Clearance Center ("CCC"). The license consists of your order details, the terms and conditions provided by Elsevier, and the payment terms and conditions.

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Figure 1 :•

 1 Figure 1: Cost model evolution

  the inputs used for the model are not maintained on a shared base, but are instantiated for each version of the model: each user has a 'unique' spreadsheet model. If a user makes changes to parameter values to adjust for new knowledge, these changes are not shared with other cost model instances in the business. Knowledge exploitation is a significant problem.Besides these elements of the industrial problem, the existing cost model has a further number of drawbacks. A significant drawback is the rather inflexible, monolithic nature of the model: the current spreadsheet implementation is focused on certain process-product combinations and does not allow for easy mixing of different materials or sub-processes. A possible route to address this would be to enable the assembly of cost model elements that are stored in a managed environment. Also, the cost model is maintained principally by only two persons, as the complexity of the model precludes more direct governability by the end users (even though the latter can still configure userdefined parameters, variation in the pre-defined parameters and formulas is discouraged). This is of course a business risk, as the full set of required modelling knowledge resides with only two persons in the organization. Both the inflexible nature of the cost model and the lack of more direct governability by the end user provide significant challenges for the exploitation and visibility of knowledge. Also, in its evolved form the cost model can be typified as a 'black-box' application. Because of its complexity and its distribution over multiple spreadsheets and spreadsheet tabs, a user effectively has no other option than to trust the model output. The user must go to considerable lengths to retrieve the original knowledge sources behind the implemented formulas and parameters; the

  International Journal of Computer Integrated Manufacturingaddressing the elements of knowledge life-cycle, exploitation and visibility. The central idea for the framework is to store the cost model knowledge in a managed environment and integrate this managed environment with the spreadsheet application in which the current model is embedded. This allows for assembly of a cost model and its managed evolution (see Figure2), as opposed to the existing cost model evolution (Figure1).

Figure 2

 2 Figure2expresses that the user can access the managed environment and retrieve

Figure 2 :

 2 Figure 2: Managed cost model evolution

Figure 3 :

 3 Figure 3: Cost model -Generic Process

•

  User definitions: These are the values that the user of the cost model can modify to drive the cost model. They are usually choices that modify the behaviour of the cost model formulas through the use of "IF-THEN" rules.

  on the basis of the composite manufacturing technology, which incorporates material type and material processing form: similar models exist for different materials and forms, e.g. CFRP prepreg, CFRP dry fibre or CFRP sandwich.Besides this elementary subdivision, the cost models themselves are organised according to manufacturing breakdown structure and manufacturing processes. The breakdown structure is a hierarchical breakdown of assemblies and products. In the cost model, on a product level similar calculations are performed for different products, for instance the bottom and top covers of the wing box. Furthermore, similar manufacturing processes are employed for parts of these products. For instance, the T-stringer production from Table3is used in both bottom and top cover production. An inspection of the full cost model has shown that the basic semantic elements of composite manufacturing technology (a combination of material and form), manufacturing breakdown structure and manufacturing process are sufficient to describe the context of a knowledge element, under the assumption that these contextual elements are developed into more detailed hierarchies for actual use (see Section 4.3). Reassuringly, this categorization conforms to the basic elements utilized for the Genetic Causal costing methodology advanced byCurran et al. (2006b).

  into the contextual model under the top-level headings of composite manufacturing technology, manufacturing breakdown structure and manufacturing process. The overall top-level contextual description model for the cost model, consisting of three primary context dimensions, is given in Figure4. This contextual model serves as the basis for the hierarchy adopted in the KM application; the three contextual dimensions are the major classes in this hierarchy (see Section 4).

Figure 4 :

 4 Figure 4: Contextual description model for cost modelling of composite structures

  the cost model. The building blocks are categorized according the previously introduced contextual model. To support this, the context dimensions of composite manufacturing technology, manufacturing breakdown structure and manufacturing process have been developed into hierarchies that offer the necessary classification richness (see Figure 10 in Section 4.3 for an example of this). Using these classification hierarchies allows for unique combinations of knowledge elements in order to find a specific building block. • Search & Retrieval: the classifications established in the previous step are used to search for building blocks in the managed knowledge environment. Various search techniques (standard text search, contextsensitive search, etc.) can be used. In the case of context-sensitive searches, multiple consecutive search choices can be made along hierarchical trees to filter down towards the desired end result. The found building blocks can be selected and consequently exported towards the spreadsheet application.• Cost Model Composition: the building blocks are used to construct an integrated cost model. The proposed solution must be able to manage duplicated knowledge elements when multiple building blocks with similar knowledge inputs are selected and exported. Also, the solution must have the capability of coping with changes in the spreadsheet environment and feeding back these changes into the knowledge base. Of course, this process must be subjected to a validation process: updates in

Figure 5 :

 5 Figure 5: Implementation architecture

  A function is included to compare the cost model elements with the knowledge contained in the knowledge base; if the user makes changes in the open spreadsheet, they can then compare the resulting changes with the original choose to update the knowledge base based on the outcome.

Figure 6 :

 6 Figure 6: Overview of solution principles (left) and integrated solution (right)

  complies with vital knowledge requirements by focusing on traceability, security and knowledge life-cycle management provision. Traceability and knowledge life-cycle management are achieved through various knowledge article features, including author of version history, status accounting through user group authorization and article status management. Security provisions include amongst others: password authorization, user rights administration, and knowledge base rights administration. Each knowledge element (e.g. parameter or formula) has been stored into its own AKM knowledge article, which retains the traceability, security and life-cycle management provisions described above. Each knowledge article has the facilities to represent not only the formal knowledge (parameters, formulas), but also the informal and/or tacit knowledge behind the formal knowledge (e.g. rationale for formulas -see Figure7for an example knowledge article 2 ). The knowledge articles are subsequently grouped into specific building blocks. Therefore, one knowledge article can be used in multiple building blocks.

Figure 7 :

 7 Figure 7: AKM knowledge article example

Figure 8 :

 8 Figure 8: Contextual model -CFRP Wing Top Cover: T-stringer production

Figure 9 :

 9 Figure 9: AKM search environment -hierarchical knowledge representations

Figure 10 :

 10 Figure 10: Knowledge-based cost modelling solution -user process

  solution has advanced both research and business interests by addressing functional needs in enabling: a) the ability to manage the evolution of the knowledge from a lifecycle perspective; b) the ability to exploit cost modelling knowledge by fitting it into the existing business process and through seamless transfer of knowledge elements into a working cost model; and c) high visibility to the knowledge used in the cost model. The developed solution has been functionally validated as shown in the application example (Section 5.2) and has been accepted and validated by a world class industry lead as a distinct improvement upon the previous cost modelling solution.

  .C. Verhagen et al.

  Contextual description model for cost modelling of composite structures 100x45mm (600 x 600 DPI) Disciplines versus fidelity (modified from Price et al., 2006) 54x20mm (600 x 600 DPI)

  Reprinted from Publication title, Vol /edition number, Author(s), Title of article / title of chapter, Pages No., Copyright (Year), with permission from Elsevier [OR APPLICABLE SOCIETY COPYRIGHT OWNER]." Also Lancet special credit -"Reprinted from The Lancet, Vol. number, Author(s), Title of article, Pages No., Copyright (Year), with permission from Elsevier."

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Make

Table 2 :

 2 Disciplines versus fidelity (modified from[START_REF] Price | An integrated systems engineering approach to aircraft design[END_REF]

	F o
	r
	P
	e e r
	R e v i e w
	O n
	l y

  Users will not optimally (if at all) acquire and use this knowledge in their daily practice; the visibility of knowledge will be too low.

	2007). The required visibility of knowledge enabled through KM is only achieved if
	users accept the knowledge management solution. As mentioned before, this is
	complicated in practice as many knowledge-based applications are perceived as 'black-
	box' applications: in short, the user does not know what is going on inside the application F o and has to rely on the results. A survey by Bermell Garcia & Fan (2008) indicated that
	r
	one of the most important business functions to be supported by knowledge-based P engineering systems is to 'increase the transparency of KBE application functionalities e and data processing'. Another critical aspect in achieving visibility is that users have to e r be involved in knowledge updating. Again, this observation is supported by Bermell
	Garcia & Fan, as 'the increase of efficiency in maintaining and updating KBE R applications' is ranked second on importance for support. Knowledge updating and e v w e i maintenance is obtained only if knowledge management provides a real added value to
	O n
	l y

note, 'the exploitation of Knowledge Management within an engineering context … is about providing the right information

[sic]

to the engineer, at the right time, in the right format, in a collaborative environment that promotes learning within the organization'. When a user is in a given context the business tool must include the relevant knowledge in this context. Moreover, this knowledge must support decision making and can sometimes even partially automate decision making. If implemented otherwise, knowledge remains in a separate application which can be subsequently be typified as black-box

(Kulon et al., 2006, Choi et al.,

  calculations to arrive at cost estimates (e.g. through the simple step of multiplication of process times with labour rates) and logistical implementation (e.g. capital outlay). The actual values for the calculation parameters in Table3have been excluded for confidentiality reasons. A further important element to note is that the aforementioned detailed process parameters (e.g. 'set time', 'weight', 'slab perimeter') are themselves dependent on a string of calculations that are driven by pre-defined and user-defined parameters, as well as the aforementioned geometric information. By and large, the rates

	F o
	r
	P
	e e r
	R e v i e w
	O n
	l y

and constants in the right-most 'Calculation' column are based on 'assumed parameters' that almost invariably lack justification of the supporting knowledge.

Table 3 :

 3

Predefined Manufacturing Process: T-stringer Production -Initial Parameters

  et al., 1996. Supporting conceptual design based on the function-behaviorstate modeler. Artificial intelligence for engineering design, analysis and manufacturing, 10 (4), 275-288.

	International Journal of Computer Integrated Manufacturing
	documents/affordablecompositesmanufacturingcompflyer.pdf [Accessed 02-03-
	2010]
	Umeda, Y.,
	o F o F o
	r r r
	P P P
	e e r e e r e e r
	R e v i e w R e v i e w R e v i e w
	O n O n O n
	l y l y l y

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URL: http://mc.manuscriptcentral.com/tandf/tcim Email:ijcim@bath.ac.ukInternational Journal of Computer Integrated Manufacturing

Acknowledgments

The authors would like to thank the various involved partners in the Next Generation Composite Wing -Multi-Disciplinary Optimization of Wing project for their financial support of this research; we would like to thank Airbus UK in particular. We would also like to thank our EADS IW and Airbus UK collaborators, in particular Simon Astwood, Alistair Dare-Edwards, Kiran Krishnamurty, Dave Gore and Tom Gibbs, for their support.

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P Hough

P Hayden

C Fallon

T J Kelly

C Mcloughin

P Yeates

J P Mosnier

E T Kennedy

S S Harilal

J T Costello

Ion Emission in Collisions between Two Laser Produced Plasmas

Measurements of the total ion emission from a pair of colliding laser-produced aluminium plasmas were obtained with the aid of a Faraday cup detector. The energy profile width at half height of the kinetic energy distribution for ions emitted normal to the target was found to be 30% narrower for colliding plasmas compared to a single plasma. Similar to ion emission from single plumes, the mean ion kinetic energy is observed to increase with the energy of the incident laser pulse. However, the width of the ion energy distribution increases at a significantly slower rate than in the single plume case.

INTRODUCTION

A laser-produced plasmas (LPP) is formed when the output pulse from a high power laser is focussed onto a dense target at an irradiance typically in excess of 1 GW.cm -2 . LPP have been the focus of strong fundamental research interest since their discovery in the 1960's [1] and have spawned a wide range of applications including laser induced breakdown spectroscopy, LIBS [2], pulsed laser deposition (PLD) [3], tabletop sources of short wavelength light [4], ion sources [5], high harmonic generation [6] and laboratory simulations of astrophysical plasmas [7][8][9].

When two plasmas collide, under appropriate conditions, as outlined by Rambo et al. [10], a layer of stagnated plasma is formed at the collision front. Outside these conditions the colliding plasmas undergo interpenetration where the plasmas pass through each other without stagnating. Rambo et al. introduced the so called "collisionality parameter," ζ, to determine whether stagnation or interpenetration will dominate in colliding plasmas. The collisionality parameter is given by

ξ = L λ ii
where L is the typical plasma dimension (i.e. the separation between the two colliding plasmas) and λ ii is the ion-ion mean free path given by [11] where m i is the ion mass, ν 12 is the relative collision velocity, e is the charge of the electron, Z is the average ionization state of the plasma, n i is the average plasma ion density, and ln Λ 12 is the so-called Coulomb logarithm [START_REF] Goldston | Introduction to Plasma Physics[END_REF] for collisions between the individual plasma plumes.

Colliding laser-produced plasmas were first investigated, to our knowledge, in the mid 1970s [START_REF] Rumsby | [END_REF]. Significant work was carried out subsequently on high-energy colliding plasmas with laser intensities ~10 14 W cm -2 [14], especially, but not exclusively on, indirect drive fusion [15].

In indirect fusion devices a hollow hohlraum hosts multiple colliding plasmas as X-ray sources which are used to drive fusion in a fuel cell located at the centre of the hohlraum [16]. Colliding plasmas have also shown much potential as laboratory scale models of astronomical interactions where, for example, Gregory et al. [17] and Smith et al. [18] have shown how they can be used as a scaled model of astrophysical colliding shocks. At lower laser intensities, in the range of interest here, colliding plasmas show real promise for applications in thin film deposition. For example, recently droplet free films were successfully fabricated using colliding laser produced plasmas [19].

To date we have reported a number of time resolved spectroscopic and imaging studies on the evolution of low energy laser produced colliding plasmas [20][21][22][23]. In this paper we change our focus to the ion emission from the colliding plasma system. In particular we extract the angularly resolved ion energy distributions. In fact a lot of work has been carried out using ions from laser produced plasmas for ion bunch injection into accelerators for medical, industrial and research and development purposes [24][25]. Areas such as ion implantation [26] and surface etching [24] have benefited significantly from the development of the laser ion source. Other potential applications of laser ion sources are varied and growing with significant effort being invested in areas such as cancer therapy [27]. Hence, the results reported here may be of interest to several apposite groups.

EXPERIMENT

The experimental scheme is illustrated in figure 1 showing how laterally colliding plasmas were used to generate the ions. A Nd:YAG laser beam of wavelength 1064 nm and a whole beam energy of 600 mJ, with a pulse width of 6 ns (full width at half max (FWHM)), was split into two equal parts and focused to two spots (i.e. 300 mJ at each focal point) separated by a distance of 1.3 mm on an aluminum slab target. The spotsize at each focus was ~100 µm yielding an irradiance of 3 × 10 11 W cm -2 and we refer to the pair of plasmas so formed as "seed plasmas".

A Faraday cup, which could be rotated about the target normal in the horizontal plane (as illustrated in figure 1) with an accuracy of ±1°, was placed so that it directly faced the target at a distance of 10 cm from the target. The entrance aperture was a 2 mm circular hole and a bias voltage of -30 V was applied to the cup to collect the ions. The signals were collected across a 50 Ω resistor coupled to a digital oscilloscope operated in single shot mode. All experiments were performed at a base pressure of 1 × 10 -5 mbar. The target was mounted on an in-vacuum high precision x-z motorized stage and was moved to reveal a fresh surface after each laser pulse.

RESULTS AND DISCUSSION

Ion emission was measured in both single and colliding plasma experiments. To generate the single laser plasma plumes, we simply blocked one of the split laser beams before it reached the target. In the case of colliding plasmas, a stagnation layer results from a rapid accumulation of seed plasma material at the collision front between the two plumes [21]. As shown in figure 2 (main), the TOF profile observed for the colliding plasmas was found to be noticeably narrower than that obtained from either of the individual seed plasmas which have almost identical profiles.

In figure 2 (insert) the TOF scale is converted to kinetic energy and it reveals a redistribution of the translational energy of the ions emitted in the colliding plasma case into a narrower profile compared to the individual single seed plumes. The low energy tail present in the kinetic energy distribution for the single plasma case is significantly attenuated in the colliding plasma case, resulting in a narrower and more symmetric distribution. Typically, the ions emitted from a single laser produced plasma, possess an asymmetric distribution [28]. The distribution for the seed plasmas here ranges from ~0.1 to 3 keV with a width (at 50% of the profile height) of 2.6 keV while exhibiting an asymmetric profile. On the other hand, the ions from the colliding plasmas exhibit a width (at 50% of the profile height) of 1.8 keV (30% narrower than a seed plume) with a quite symmetric distribution. We can also see from figure 2 that the (instantaneous) peak current from the colliding plasma is enhanced approximately three fold compared to that of a single plasma or by ~50% compared to the numerical sum of the left and right seed plumes.

Figure 3 shows the dependence of the integrated TOF signal, normal to the target surface, as a function of incident laser energy. The data points were obtained by integrating the traces in figure 2 for total laser energies in the range 100 -600 mJ. A departure from linearity is observed in the case of the colliding plasma system compared to the single seed plumes. It is evident from figures 2 and 3 that the ion emission, normal to the target, from the colliding plasmas is clearly not a simple numerical sum of those from the seed plumes. Although the full explanation for our observations will require detailed modeling we suggest a couple of processes which will need to be taken into account. In the early stages of stagnation layer formation (first few tens of nanoseconds) we know that electron stagnation occurs [21] and so the prompt highly charged ions from the seed plumes are likely to experience a local accelerative force. Hence we would expect to see an increase in the number of ions at higher kinetic energies close to the peak or cut-off energy. However, as time progresses, ion stagnation is established, and the stagnation layer can build up a net positive sheath which can result in Coulomb blocking of the slow ions from each seed plasma. Additionally, as time proceeds, slow ions can be lost in collisions with the stagnation layer. We suggest that all of these processes (and perhaps others) can act in consort to reduce the low energy ion flux.

Figure 4 (main) shows the angle-resolved ion TOF signal from the colliding plumes. A narrower energy distribution is observed only in the direction normal to the target and the angleresolved integrated flux can be fitted by a cos n function [29] similar to the single plasma plume case [30]. It is also evident from figure 4 that the ion signal, in the colliding plasma case, splits into two distinct peaks, labeled P1 and P2, when the Faraday cup is moved to angles between 5°a nd 30°either side of the target normal. The first peak, P1, is due to ions emerging from a seed plasma plume and arriving at the detector which appear to be largely unaffected by the presence of the stagnation layer. This is clear from figure 4 (inset) where it can be seen that the first peak, P1, of the colliding plasma signal matches extremely well with the ion TOF signal from the left seed plasma plume only. The amplitude and kinetic energy of P2 decrease rapidly as the detector is rotated away from the target normal. Beyond ± 20°P2 is severely diminished and has disappeared completely for angles greater than ± 30°where the traces are indistinguishable from the single plasma plume case. Therefore, we conclude that P2 is due predominantly to ion emission sensitive to presence of the stagnation layer which, as can be readily observed in figure 4, is highly directional. This observation stands in stark contrast to emission from single plasma plumes where ions are emitted over a wide range of angles.

Figure 5 reveals the dependence of the peak, i.e., the most probable kinetic energy and the distribution width of the ions emitted from single and colliding plasmas as functions of the incident laser pulse energy, E L . It is clear from figure 5 that by varying E L it is possible to tune the kinetic energy distribution of the ions emitted from colliding laser produced plasmas. The peak position of the ion energy distribution from colliding plasmas (blue diamond) increases linearly with E L . However, the profile width at half height (PWHH) of the distribution for colliding plasmas is proportional to E L 1/2 . Hence relatively narrow profiles (referenced to the peak position) can be obtained with increasing laser energy. In contrast, in the case of a single plasma, we observe that both the peak position and the PWHH of the ion energy distribution increase linearly with E L for these plasma regimes.

In conclusion, we have measured the angularly resolved ion emission from a laser produced colliding plasma system. The ions emitted from the stagnation layer, formed in the vicinity of the collision front between two colliding plasmas, were found to possess a narrower and more symmetric kinetic energy distribution than for the single laser plasma case. Both the peak energy and profile width could be adjusted by varying the incident laser energy. The linear dependence of the peak position, and the square root dependence of the profile width, implies a sharper distribution with increasing laser energy. In the future we will extend these studies to include charged resolved measurements, with a compact retarding field analyser currently under development at our laboratory.

Figures and Captions

  Figure 2 (main) shows a comparison of the ion time of flight (TOF) signals collected for the colliding plasmas system (dark blue trace) along with those for the single seed plasma cases (red and green traces). The numerical sum of the left and right plasma distributions is also shown in the figure (black trace). The corresponding kinetic energy distributions derived from the TOF profiles are shown in figure 2 (insert).

1 .

 1 Figure 1. Schematic diagram of the experimental configuration (not to scale). The seed plasmas were separated by a distance of 1.3 mm, the Faraday cup was located at a distance of 100 mm from the target and the entrance aperture of the Faraday cup was 2 mm in diameter.

Figure 2 .

 2 Figure 2. (Main) Ion TOF signals for colliding plasmas (dark blue trace) and single seed plasmas (red and green traces). The Faraday cup was positioned normal to the target. (Insert) TOF signals converted to kinetic energy distributions for single 'seed' plumes and colliding plasmas system.

Figure 3 .

 3 Figure 3. Variation of the time integrated ion TOF signal, normal to the target surface, with incident laser energy for colliding plumes. In the case of the left and right seed plasmas the total energy was split equally between them, so the energy in each focal spot was half the value shown on the horizontal axis.

Figure 4 .

 4 Figure 4. Main: Angle-resolved TOF signal for colliding plasmas for a range of angles of detection (main). Insert: Comparison of the colliding plasma TOF signal with that of a single plasma plume at a detection angle of 20°.

Figure 5 .

 5 Figure 5. The variation of the Profile Width at Half Height (PWHH) and peak position of the ion energy distribution normal to the target with incident laser energy for colliding plumes. The arrows point to the relevant axes for each trace. In the case of the single plasma plume the total energy into the focal spot on the target was 100% of the value shown on the horizontal axis. In the colliding plasma case the total energy in each of the seed plasmas was 50% of the values shown.

ACKNOWLEDGEMENTS

This work was sponsored by Science Foundation Ireland under grant numbers Science Foundation Ireland (SFI) RFP PHY041 and SFI PI 07/IN.1/I1771 and the Higher Education Authority (HEA) PRTLI IV INSPIRE program of the 2nd National Development Plan (NDP2). PHo and TJK thank the Irish Research Council for Science Engineering and Technology and SSH the DCU International Visiting Fellows Program for financial support. Work done in the frame of EU COST action MP0601.