Computers in Industry 56 (2005) 125–140 www.elsevier.com/locate/compind
Knowledge web-based system architecture for collaborative product development Karina Rodriguez, Ahmed Al-Ashaab* School of Engineering and Built Environment, University of Wolverhampton, Wulfruna Street, Wolverhampton WV11SB, UK Received 22 April 2003; accepted 5 July 2004 Available online 1 October 2004
Abstract The manufacturing competitive environment has intensified in recent years. In this environment, companies do not possess all the knowledge they need but instead rely on other organizations. This results in the need of distance product development, which in turn requires information and knowledge in the place, time and format required. In response to this need the research community has come with a solution called collaborative product development (CPD) systems. This paper introduces the partial results of the ongoing research to propose a knowledge driven CPD system architecture, which will facilitate the provision of knowledge involved in product development. This paper presents the research issues and industrial requirements for such system. Furthermore, the proposed system architecture is described in detail and its implementation is presented using a case study of an injection moulded product. # 2004 Elsevier B.V. All rights reserved. Keywords: Collaborative product development; Manufacturing model; Injection moulding process information; Knowledge web-based engineering
1. Introduction The manufacturing competitive environment has intensified dramatically and expanded globally in recent years. This trend has been principally driven by world open market and growing customer expectations for products delivered quickly and at competitive prices. In this global environment, organizations do * Corresponding author. Tel.: +44 1902 32 22 76; fax: +44 1902 32 27 43. E-mail address:
[email protected] (A. Al-Ashaab).
not possess all the knowledge they need but instead rely on buying technologies or services through contractual and cooperative partnerships with other organizations [1]. The use of this approach results in the need of distance product development, which in turn requires the provision of product life cycle information and knowledge in the place, time and format required. In response to this need the research community has come with a solution called collaborative product development (CPD) system, which is defined as: ‘‘an Internet based computational architecture that supports the sharing and transferring of
0166-3615/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2004.07.004
126
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
knowledge and information of the product life cycle amongst geographically distributed companies to aid taking right engineering decisions in a collaborative environment’’ [2]. Among the existent technologies to support collaborative product development, the focus has been in sharing product data and providing collaborative tools to bring the multidisciplinary team together. However, there is still the need to capture and share the know-how of the geographically distributed partners. The knowledge involved in this research is related to the technological constraints that affect the decisions taken when developing a product. For example, manufacturing process and resources constraints that must be considered for the development of injection moulded plastic products. This paper presents a knowledge driven collaborative product development (KdCPD) system architecture that addresses the requirements, as defined by both the research and industrial communities. Section 2 describes the methodological approach used in the research. Sections 3 and 4 show the research issues and industrial requirements of the collaborative product development. The mentioned industrial requirements have emerged from an industrial survey conducted in three injection moulding companies. Section 5 describes in detail the proposed system architecture and its elements. Section 6 presents its implementation using a plastic injection moulded part as a case study. Conclusions are presented, finally, in the last section.
2. The research methodology The research approach that has been adopted in this work is illustrated in Fig. 1. The different activities of the research were conducted as follows: a. An extensive literature survey was performed in order to identify the characteristics of the systems that support collaborative product development (see Fig. 1a). The analysis helped to pinpoint several technological requirements. b. Parallel to this, the industrial requirements were identified by performing a survey in three injection moulding companies within the UK (see Fig. 1b). The results were mapped with the previously
c.
d.
e.
f.
identified research issues and a list of requirements for a CPD system was produced. As Fig. 1c shows, CIMOSA [3] was chosen as reference architecture because it is considered to be clear and flexible to model the activities, information, knowledge, locations and organisation point of views in order to support collaborative product development. The formal modelling techniques, such as IDEF0 for activity modelling and UML for information modelling, were used to represent and describe the above point of views. The activities and knowledge were modelled using the information acquired from the injection moulding companies approached during the industrial survey and from the literature review (see Fig. 1d). A KdCPD system architecture that addresses the research issues was developed. The architecture is presented in detail in this paper. Finally, a prototype of the proposed KdCPD system is being implemented and some of the results are presented.
The next section will describe in more detail the literature survey undertaken to identify the characteristics of CPD systems.
3. Technological requirements of CPD systems A number of research initiatives related to Internet based collaborative product development systems have been undertaken by several authors. The literature review has highlighted several technological requirements that must be addressed in order to develop enabling technologies for this type of systems. These are: 1. Information system architecture: information models and engineering applications are integrated within a framework in a structured and transparent manner using communication protocols between the elements of the system [4]. 2. Communication tools: tools to enable the visual/ audio communication amongst geographically distributed team members. 3. Virtual team management tools: to coordinate the distributed team members.
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
127
Fig. 1. Methodology to develop an Internet based architecture to support collaborative product development.
4. Product model: a software representation of form and data that describe a product throughout its life cycle [5]. 5. Engineering applications: software to support the correct engineering decision making throughout product development. 6. Product geometric representation: software application that facilitates the visualization of product design among the geographically distributed team members. 7. Integration with CAD/CAM/CAE commercial software: interface applications to import/export files from commercial CAD/CAM/CAE systems. 8. Knowledge representation: the documentation of learning lessons and other generic rules, which are stored in a repository of information. 9. Project management tools: to coordinate product development activities. Table 1 exhibits the reviewed CPD systems illustrating the technological requirements they support. The following present in more detail the four key technological requirements that the authors believe are
needed in any CPD system. These are communication tools, engineering applications, product model and knowledge representation. 3.1. Communication tools In order to support communication between distributed team members the reviewed systems provide synchronous and asynchronous collaborative tools. Synchronous tools are used for real time communications, such as video/audio conferencing, whiteboard, chat sessions and sharing geometric models to provide a virtual meeting environment. Asynchronous tools are used in non-real time communications, i.e., email or file downloading from a database. 3.2. Engineering applications Effective collaborative product development could be achieved by using engineering applications that support the correct engineering decision making. These are the applications that need to be performed collaboratively.
128
Table 1 Features of a collaborative product development system included in the reviewed systems CPD systems
Technological requirements Information Communication system tools architecture
Chung and Kunwoo [9] SOMF by Domazet et al. [10] CODES by Gupta et al. [11]
* * *
Design for X by Shi et al. [27]
*
NetFEATURE by Jae et al. [25] CyberView by Kim et al. [19] EDSE by Li et al. [12] STARS by Lu and Cai [13] WPDSS by Qiang et al. [22] Qin et al. [14] Enterprise-Web by Rezayat [15] Roy et al. [20]
*
*
* *
* *
*
Whiteboard * Videoconference
*
* *
*
*
DCEE by To¨ rlind [16] CyberEye by Zhuang et al. [17]
Whiteboard, visualize geom.
* *
*
Conceptual design
*
* *
Conceptual design Conceptual design
Visualize geom.
Collab. Studio by Sevy et al. [21] * Su D. et al. [23]
*
Product Integration Knowledge Project geometric with CAD/ representation management representation CAM/CAE software
Audioconference, whiteboard Audioconference, whiteboard, visualize geom. Videoconference, visualize geom. Visualize geom. *
*
*
Conceptual design, design for X, manuf. process planning Conceptual design Conceptual design Conceptual design, engineering analysis, manuf. process planning Conceptual design, design for X, manuf. process planning Conceptual design Conceptual design Conceptual design Conceptual design Conceptual design Conceptual design Conceptual design Conceptual design, design for X, eng. analysis, prototyping, manuf. process planning Conceptual design
* *
*
* * *
* *
*
* *
* * *
* * * *
* *
*
*
Conceptual design, manuf. process planning
*
*
Conceptual design
*
*
Conceptual design
*
*
*
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
DOME by Abrahamson et al. [6] * DISCS by Anderson and * Abdalla [7] Biennier and Favrel [8] * WebCADET by Rodgers * et al. [26] Chang et al. [18] *
Virtual Product Engineering team model applications management
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
129
Fig. 2. Different approaches to support collaboration during design.
Table 1 illustrates different engineering applications provided by the reviewed systems. As shown in the table, most of the effort has been directed to support the design activity. This activity involves collaboration and, therefore, extensive communication is required among the team members in order to create, analyse and evaluate design alternatives. The following three approaches have been supported by the researchers: a. Common access of design data: the collaboration is achieved by sharing product data [6–17]. There is no real time visualization of the geometry. The data, mainly design data, is downloaded from an information system (see Fig. 2a). b. Collaborative visualization of the component: as shown in Fig. 2b, this approach allows the engineers to convert the solid model previously designed into a 3D virtual geometric model. Such a model can be visualized in real time, but not modified, over the Internet [16–21]. c. Collaborative design of the component: this approach allows the geographically distributed designers to visualize and modify the product
geometric model in real time (Fig. 2c). Qiang et al. [22] and Su et al. [23] propose a system where the designers work together with the same solid model in a commercial CAD system. Other commercially available initiatives, known as collaborative product commerce systems, use a similar approach, e.g. [24]. 3.3. Product model The product data is used and produced by different engineering applications throughout the product development process. The data is usually stored in what is called a product model. The structure of any product model is related to the engineering application that it supports. As presented in the previous section, most of the reviewed CPD systems are concerned with the design activity. Therefore, most of the product models have been structured to capture product design data, mainly geometric data and BOM, using the following form: Based on ISO standard STEP 10303 AP-203 [7,10,16,19].
130
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
Based on non-standard structure and implemented with commercial databases, Chang et al. [18], Jae et al. [25], Gupta et al. [11], Qin et al. [14] and Roy et al. [20] developed their own models to represent the product design data and manufacturing data. The information captured includes component features [18,25,20], geometric data [18,11,20] and machine tools [20]. 3.4. Knowledge representation In order to have an accurate and faster decision making support with some level of automation, knowledge related to product development should be captured. There are different opinions regarding the definition of this knowledge. The authors have classified the knowledge in the following types: 1. Product data: such as product specifications, CAD file, design analysis and market studies. This type of knowledge has the disadvantage that the data still needs to be analysed and applied to the specific problem. 2. Previous cases history: the data about past projects and the rationale about how decisions were taken is also considered useful to take decisions during current projects [12,21]. This approach is time consuming because the relevant information needs to be found, understood and applied. 3. Product life cycle constraints: the decisions taken during the development of a product may be limited by technological, processes, resources, material or other considerations. For example, to design an injection moulded component there are certain characteristics of the process that need to be considered, such as the capability of only producing thin walled products. This knowledge is available most of the time from the experience of the engineers, in books or other documents. Research effort [18,20,26,27] has been made to capture design and manufacturing rules in the form of ontologies or artificial intelligent rules to support isolated applications. However, the proposed systems do not provide the capability to share these rules in real time or through direct interaction with the engineering applications. One of the approaches adopted is to store the constraints in a database and provide a search engine.
4. Industrial study of collaborative product development After analysing the technological requirements for a CPD system, an industrial survey was conducted amongst engineers of three manufacturing companies. In this particular research, the companies selected are involved in some aspects of plastic injection moulding, such as product design, mould design and fabrication, as well as the processing of the plastic parts. This is because the engineering application of the presented CPD system is injection moulding. The survey was conducted by means of a questionnaire, which was designed based on the information collected during the literature review. The objective was to understand the industrial need of collaborative product development, in addition to the following specific objectives: Investigate whether there is an industrial need to collaborate with the customer, supply chain and other partners. Understand the current mechanism of communication between the companies and their supply chain when such collaboration exists. Investigate the best mechanisms to achieve effective collaboration according to the industrial needs. 4.1. Results of the industrial survey One of the main findings is that the distance collaboration amongst the companies of the supply chain is crucial due to the globalisation of the market and their involvement in international manufacturing alliances. Such results are illustrated in Fig. 3a, where 100% of the interviewed engineers considered the collaboration either important or crucial in the current product development practice. To achieve an effective collaboration it is required to use real time distance communication tools, as previously explained. Fig. 3c shows the results of the currently used tools and those that are desired to support distance collaboration with other partners. It is evident that the Internet communication tools are favoured, especially email, sharing of product information and geometric data. The required product information in a typical collaborative activity is illustrated in Fig. 3b. Specification data, parts and
131
Fig. 3. Findings of the industrial survey.
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
132
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
component information, geometry data, bill of materials, test data and design information are examples of such product information. 4.1.1. Mapping of technological requirements and industrial findings. The industrial survey identified several requirements that were mapped with those of the literature survey. From this mapping a set of requirements for a CPD system was deduced: The engineers considered that it is crucial to have effective collaboration with their geographically distributed partners. Hence, an Internet based computer system is imperative. Such a system requires an architecture, which is distributed, interoperable, secure and modular. A major requirement that has not been addressed by any research group is the capturing of knowledge and its delivery in real time to support engineering decision making. This knowledge should be captured in manufacturing constraints, such as process, material and resources capabilities. The industry needs a CPD system that supports the complete product life cycle. Hence, the need to capture and share product information could be addressed by having a product model. Such a model must capture and provide all product life cycle data (i.e. product engineering data, manufacturing and tooling and testing data) in real time. The current CPD systems are mainly focused in supporting the design application while the industrial survey highlights the need for other key applications that should be performed in distance. As such, future CPD systems must support a range of engineering applications. For example, design for manufacturing and selection of production equipment. The provision of communication tools has been well addressed in the current research. However, two main points should be emphasised in future generations of CPD systems. First, the distributed team should share geometry in such a way that it could be modified in real time; and second, geometric data should be integrated with the decision support engineering applications. Project management applications are required to coordinate the virtual team and their tasks. This
issue has not been emphasised in the current CPD systems. To develop a CPD system with all the above characteristics, it is necessary to have enabling technologies. In this research, these technologies were selected according to CIMOSA (Open System Architecture for Computer Integrated Manufacturing), which was selected as the reference system architecture. The proposed architecture is described in the following section.
5. Proposed knowledge driven CPD system architecture An Internet based system architecture is proposed in this paper to support collaborative product development, while its application is presented in Section 6. As shown in Fig. 4, the architecture is structured in a three-layered framework: information, application and end user layer. In such a system, the end user layer is situated in the user’s desktop and is connected to the application Web server (application layer), which in turn is connected to the information databases (information layer). The following sections will describe each of the layers in more detail. 5.1. Information layer of KdCPD system architecture 5.1.1. Product model In order to support the whole product life cycle, as required by industry, the product data should be structured in a product model, which in this research is based in a feature-based approach [28]. This approach facilitates the integration with the manufacturing knowledge and supports a range of engineering applications. The product data is provided in real time, and it captures the development progress. Product data is also visualized through 3D virtual product geometry as shown in Fig. 6. 5.1.2. Manufacturing knowledge model Decision making during product development is difficult as decisions need to be taken in collaboration with other companies that do not have access to the knowledge of their distributed partners. To
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
133
Fig. 4. Knowledge collaborative product development system architecture.
overcome this issue, it is necessary to have a distributed source of knowledge to support the different activities. The manufacturing knowledge model [29] addresses such industrial requirement because it is an information model that captures process and resources capabilities. Its manufacturing data integrity is captured as a result of the way the model represents the manufacturing constraints imposed on the product data definition. The manufacturing knowledge model is the source of information required to support the decision making during the engineering applications presented in the application layer section. In addition, the impact of one engineering decision on other applications is highlighted due to the interaction between the data captured in the model.
5.2. Application layer of the KdCPD system architecture The application layer consists of two elements: decision support engineering applications and information management tools. Details of each element are presented next. 5.2.1. Decision support engineering applications The application layer provides a range of key product life cycle applications that need to be preformed in a collaborative manner. As a result, the system supports a range of engineering and manufacturing activities as emphasised during the industrial survey.
134
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
This research is concerned with the injection moulded product development; hence the proposed decision support engineering applications are project management, specification definition, plastic product engineering, process engineering, injection mould design and fabrication. Each of these applications contains sub-applications in order to provide the specific support during product development. It is important to mention that due to the provision of both product data and manufacturing process information, the engineering applications provide: a level of automation when taking a decision; the capability to be performed in parallel; the flexibility to move from one application to another without the need to follow a rigid sequence assuming there is sufficient product data available. The need of capturing and delivering knowledge in real time is fulfilled by providing advice based on relevant knowledge during the product engineering, process engineering and tool making applications. In addition, the end users are provided with collaborative tools in order to maintain communication amongst the distributed team. NetMeeting is used as the communication mechanism in the implementation of the system, as explained in Section 6.2.3. The following sections will describe the elements of the architecture, which are being implemented as presented in Section 6. 5.2.1.1. Project management applications. This application provides the involved team members with a project timing plan, which includes tasks status, times and required resources [30]. 5.2.1.2. Specification definition applications. This application is concerned with capturing customer requirements in order to ensure that the voice of the customer is represented throughout product development. This will facilitate performing quality function deployment. 5.2.1.3. Product engineering applications. The product engineering applications consist of several subactivities. These are design session, design for manufacturing, FMEA and cost calculation. These applications are implemented in the system to be
performed in a collaborative manner, as it was highlighted in the industrial survey. A description of each of them follows. During the design session application the user defines the product in terms of features, such as wall, ribs and webs. The geometric representation of the product is available in a 3D viewer. The product feature information is stored in the product model and used by different engineering applications to support decision making after invoking the required information from the manufacturing knowledge model. This is to validate that any decision taken falls within the manufacturing constraints. The design for manufacturing (DFM) application ensures that product functional features can be moulded without problems and also provides feedback to the designer whenever problems arise. The cost calculation application estimates the product development cost according to the three key elements of information: product design, material and required production resources. The failure mode effective analysis application (FMEA) identifies the potential product failure and their causes in order to eliminate them from the design. It requires the input from the product life cycle experts through a virtual meeting environment. 5.2.1.4. Process engineering applications. The selection of production equipment application selects the suitable injection-moulding machine for the production of a specific plastic part. In order to calculate the required machine size it is necessary to consider the product design information available from the product model. The process parameters application gives advice to the process engineer about the optimum operation parameters (i.e. the injection pressure, the plastic material melting temperature, the mould temperature and the cycle time) of the selected injection machine. These calculations are based on process and material capabilities captured in the manufacturing knowledge model as well as on product data and mould design information available from the product model. 5.2.1.5. Tool making applications. The mould design application uses product design data stored in the product model to give advice about the best options to define the injection mould elements, such as standard
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
plate, core, cavity, feed system (sprue, gate and runner), venting system, cooling system and ejection system. The process and resources capabilities stored in the manufacturing knowledge model support this application. The mould fabrication application advises the mould manufacturer about the best mould fabrication methods, such as machining or EDM. The design data of the plastic part and the injection mould stored in the product model is used along with the manufacturer knowledge model to produce this advice. 5.2.2. Information management applications The proposed KdCPD system is based on timely and accurate provision of information, which in turn supports the engineering applications. Hence, the need to have information management applications is to control information access, maintain the knowledge and manage the geographically distributed collaborative team. The information management application includes three main sub-applications. These are: Team management application: to capture team members data, responsibilities, expectations and their right to access the different elements of the system. Product files access application, to upload/download documents from within the product model. Knowledge management application, for the KdCPD administrator to maintain and upgrade the manufacturing knowledge model. 5.3. End user layer The end user layer forms the front end of the system. It consists mainly of a web browser, such as Internet Explorer or Netscape, to view and use the different decision support engineering applications and collaborative tools.
6. Knowledge web-based KdCPD system 6.1. The implementation of the KdCPD system The proposed system architecture is being developed as a modular-based prototype. The manufacturing knowledge model and the product model are
135
implemented as object oriented databases using the Object StoreTM [31] database management system. They reside in the back-end of the system and are accessed by the engineering applications using standard based CORBA [32] connectivity. Fig. 6 shows the web interface of the implemented KdCPD system. This interface includes a menu of engineering and information management applications located on the left side of the screen. As described in Section 5.2, the engineering applications are classified in the following activities: specifications definition, product engineering, process engineering, tool making and project management. Each of these activities contains a set of sub-applications. The information management menu contains team management, product file access and knowledge management applications. At this stage, two engineering applications have been implemented in the KdCPD system: ‘‘design session’’ and ‘‘design for manufacturing’’. In addition, other applications such as ‘‘selection of production equipment’’ and ‘‘mould design’’, are in their preliminary stage of development. The implementation of the engineering applications use object oriented technologies, such as JavaTM [33] and Java3DTM languages. Such applications contain the graphical user interface and the CORBA connection to the databases. They receive input data from the end user and send it to the databases, where the information is processed and a response is sent as feedback advice. The next section describes some of the functionalities of the system through a case study. 6.2. A case study of collaborative DFM of an injection moulded part Fig. 5 illustrates a view of the interaction between the different elements of the system architecture presented in Section 5, while Fig. 6 shows the software implementation of such system. A designer collaborates with a mould maker to consider the design for manufacturing issues of a plastic part shown in Fig. 5a. This is an electrical housing prismatic part that has three bosses for assembly purposes. The following section presents in some detail the interaction between the product model, the manufacturing knowledge model and the product engineering applications.
136
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
Fig. 5. Interactions between the end users, engineering decision support applications and knowledge and product models.
6.2.1. Design session Product development in this collaborative environment starts by selecting the ‘‘design session’’ application. Fig. 6 illustrates the typical graphical user interface, which is tailored as follows: menu to define a product in terms of features, data input fields, geometric representation area and feedback advice area. The end user needs to input general information of the product, such as product name, general dimensions, weight material and production quantity. By pressing the ‘‘OK’’ button the data is captured in the product model and the end user can start defining the product in term of features, as illustrated in Fig. 5b and c. The wall feature is considered to be the main feature of a plastic product, on which other features (e.g. ribs, bosses, holes, etc.) are placed. Each feature must have a
name and attribute, which are used throughout the analysis sessions of the KdCPD system. The end user has the option to specify whether the feature is critical or not for the part functionality. This is used to prioritise them during the DFM analysis. The feature definition is confirmed by pressing the ‘‘OK’’ button. A message is displayed in the feedback area to confirm the successful capturing of the data or any other problem. Then, the 3D virtual geometric model of the feature is displayed in the geometric representation area. The part definition is stored in the product model (see Fig. 5c) and used by other engineering applications to support decision making after invoking the required constraints from the manufacturing knowledge model as explained in Section 5.1.1. One of these applications is ‘‘design for manufacturing’’, which is presented in the following section.
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
137
Fig. 6. The graphical user interface of the knowledge driven collaborative product development system.
6.2.2. Design for manufacturing application The ‘‘Design for manufacturing’’ application is accessed by clicking the corresponding icon. The first step is to load product data from the product model. Then, by pressing the ‘‘Start DFM’’ button one of the following manufacturability analyses will start: Part DFM: the system analyses the features of the part prioritising the critical ones. Feature by feature DFM: the user has the choice to select any specific feature for its analysis. As illustrated in Fig. 5e, the result of this analysis is displayed in the feedback section and it is also stored in a file to be shared amongst the geographical distributed team. The DFM analysis is illustrated using the ‘‘Base Wall’’ defined with the following attributes: thickness,
6 mm; width, 80 mm; length, 80 mm, and without draft angle. The DFM application invokes the appropriate data from the manufacturing knowledge model to validate the manufacturability of the ‘‘base wall’’ (see Fig. 5b and d). This wall is outside manufacturability constraints, so the application sends a feedback advising to reduce the wall thickness to 1.8 mm, as shown in Fig. 5d and e. This value is based on the recommendation of the plastic material provider. The designer needs to change those values in the appropriate fields. The new data is stored in the product model by pressing ‘‘OK’’, as shown in Fig. 5c. At the same time, the system displays these changes in the virtual geometric model. In this way, the user is aware of how the manufacturing constraints directly affect the geometry of the part. The mouldability of other features, such as the boss, depends on the wall on which these are attached.
138
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
Bosses are commonly used for assembly purposes. The rules for the maximum permitted height and thickness are: Boss height ¼ ð2:5 wall thicknessÞ; 1 boss thickness ¼ ð2 wall thicknessÞ 3 As shown in Fig. 5b, a boss is defined with the following attributes: diameter, 20 mm; thickness, 5 mm; height, 15 mm, and without draft angle and base radius. The system gives feedback advice to the designers to reduce the height of the boss to 4.44 mm, the thickness to 1.18 mm, add a draft angle of 18 and a base radius of 0.58 as illustrated in Fig. 5e. 6.2.3. Other functionality of the KdCPD system After running the product engineering applications, the updated product design data is captured in the product model. This data is available to other engineering applications and distributed team members. The other applications of the KdCPD system use the same approach to support the geographically distributed team. The collaboration amongst a distributed product development team is achieved by different mechanisms: By providing real time access of both product data and manufacturing knowledge, facilitating the following: One engineer interacting with the system, while the other team members are able to observe and trace the product development by accessing the results. This case is illustrated in Fig. 5. Two or more engineers, such as designer and tool engineer, are able to use different engineering applications simultaneously to develop a product. Two designers are able to access the same engineering application to continue developing the same product at different times. By providing a tool, such as NetMeetingTM [34], to perform the applications in a collaborative environment. For this purpose, a NetMeeting session can be started during the engineering activities that require collaboration of the geographically distributed team members, such as ‘‘design session’’
and ‘‘design for manufacturing’’. NetMeeting provides the following communications tools: chat sessions, videoconference and whiteboard. 7. Conclusions The paper has presented a novel approach of a system architecture that guides the development and implementation of a knowledge driven collaborative product development system. A demonstration of its application in injection moulded product development has also been presented. The research has been conducted by adapting a practical methodology based on both findings from extensive analysis of existing CPD systems and an industrial survey. The latter has clearly shown the interest of the manufacturing companies in the area of CPD. Mapping their requirements with the findings of the reviewed systems has led to the identification of the key technological requirements. These requirements should be addressed in order to have an effective CPD system that aids solving real engineering problems. One of the main requirements, which the authors have emphasised as a major contribution for the next generation of CPD systems, is the real time provision of manufacturing knowledge. The sources of this knowledge are the manufacturing process and resource capabilities, company experience, industrial heuristic knowledge and other technical documents available from the material and machinery providers. This knowledge is stored in a manufacturing knowledge model. The web-based environment and the object oriented technologies have demonstrated to be a good development platform for the KdCPD system. In order to integrate and share the information and knowledge available in geographically distributed companies, applications based on CORBA reference model [32] have proven to be essential. In addition, the interoperability among the different heterogeneous elements of the system is also achieved by using the CORBA standard. The availability of both product data and manufacturing process information has facilitated a level of automation when taking engineering decision in a geographically distributed environment. The use of a feature-based approach in this collaborative environment has provided the integra-
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
tion between the engineering applications and the manufacturing process knowledge. However, it has limited the geometric representation of complex products. In addition, the geographically distributed team members could visualize the product data in a geometric virtual model, but its translation to a proper solid model yet needs to be achieved. The proposed approach does not aim to replace existing systems in companies but rather to be a support tool for communicating and sharing knowledge among the geographically distributed partners. The implementation of this system could be considered feasible among the partners of one industrial group or extended enterprise, who are bonded by common financial interests. Such system will lead to the production of better and more cost effective products, developed in a shorter period of time.
Acknowledgements The authors gratefully acknowledge Latmier Technologies, Arvin Meritor and Denso for their support in providing information during the initial stages of this research. In addition, the authors would like to thank Excelon for providing the object oriented database management system Object StoreTM and Dr. Reyna Al-Ashaab for her valuable help with reviewing the text of this paper. Miss Rodriguez Ph.D. research study is supported by a bursary from the University of Wolverhampton. References [1] W.C. Choo, B. Detlor, D. Turnbull, Web Work: Information Seeking and Knowledge Work on the World Wide Web, Kluwer Academic Publishers, The Netherlands, 2000. [2] K. Rodriguez, A. Al-Ashaab, A review of internet based collaborative product development systems, in: Proceedings of the International Conference on Concurrent Engineering: Research and Applications, Cranfield, UK, 2002. [3] ESPRIT Consortium AMICE, 2nd Revised and Extended Edition, Research Report, Esprit Project 688/5288, Springer-Verlag, 1993. [4] A. Molina, A. Al-Ashaab, T. Ellis, R. Young, A review of computer-aided simultaneous engineering systems, Research in Engineering Design 7 (1995) 38–63. [5] R. Young, R. Bell, Machine operation planning in product development modelling environment, International Journal of Production Research 30 (11) (1992) 2487–2513.
139
[6] S. Abrahamson, D. Wallace, N. Senin, P. Sferro, Integrated design in a service marketplace, Computer Aided Design 32 (2000) 97–107. [7] N. Anderson, H. Abdalla, Web browser based collaborative product development for the 21st century, in: Proceedings of the International Conference on Concurrent Engineering: Research and Applications, California, USA, 2001. [8] F. Biennier, J. Favrel, Collaborative work organisation in a pool of enterprises, in: Proceedings of the Conference on Advances in Concurrent Engineer, Bath, UK, 1999. [9] J. Chung, L. Kunwoo, A framework of collaborative design environment for injection molding, Computers in Industry 47 (2002) 319–337. [10] D.S. Domazet, M.C. Yan, C.F.Y. Calvin, H.P.H. Kong, A. Goh, An infrastructure for inter-organizational collaborative product development, in: Proceedings of the International Conference on System Science, IEEE, Hawaii, 2000. [11] S.K. Gupta, C.J.J. Paredis, R. Sinha, C.-H. Wang, P.F. Brown, An intelligent environment for simulating mechanical assembly operations, in: Proceedings of the ASME Design for Manufacturing Conference, ASME, Atlanta, USA, 1998. [12] G. Li, R.H. Bracewell, K.M. Wallace, A knowledge framework to support engineering design, in: Proceedings of the International Conference on Concurrent Engineering: Research and Applications, California, USA, 2001. [13] S.C.-Y. Lu, J. Cai, Stars: a socio-technical framework for integrating design knowledge over the internet, IEEE Internet Computing (2000). [14] S.F. Qin, R. Harrison, A.A. West, I.N. Jordanov, D.K. Wright, A framework of web-based conceptual design, Computers in Industry 50 (2003) 153–164. [15] M. Rezayat, The enterprise-web portal for life-cycle support, Computer Aided Design 32 (2000) 85–96. [16] P. To¨ rlind, Product Models and Environment for Distributed Engineering, Lulea˚ University, Lulea˚ , Sweeden, 1999. [17] Y. Zhuang, L. Chen, R. Venter, Cybereye: an internet-enabled environment to support collaborative design, Concurrent Engineering: Research and Applications 8 (3) (2000) 213–229. [18] H.-C. Chang, W.F. Lu, X.F. Liu, WWW-based collaborative system for integrated design and manufacturing, Concurrent Engineering: Research and Applications 7 (4) (1999) 319–334. [19] C.-Y. Kim, N. Kim, Y. Kim, S.-H. Kang, P. O’Grady, Internetbased concurrent engineering: an interactive 3d system with mark-up, in: Proceedings of the 18th ASME Computer in Engineering Conference, ASME, Atlanta, USA, 1998. [20] U. Roy, B. Bharadwaj, S.S. Kodkani, M. Cargian, Product development in a collaborative design environment, Concurrent Engineering: Research and Applications 5 (4) (1999). [21] J. Sevy, V. Zaychik, T.T. Hewett, W. Regli, Deploying and Evaluating Collaborative Engineering Studios, Proceedings of the International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Gaithersburg, USA, 2000. [22] L. Qiang, Y.F. Zhang, A.Y. Nee, A distributed and collaborative concurrent product design system through the WWW/ internet, Advanced Manufacturing Technology 17 (2001) 315– 322.
140
K. Rodriguez, A. Al-Ashaab / Computers in Industry 56 (2005) 125–140
[23] D. Su, S. Ji, J. Li, X. Chen, Web-enabled collaborative environment for integrated design and manufacture, in: Proceedings of the International Conference on Concurrent Engineering: Research and Applications, Cranfield, UK, 2002. [24] Alibre Inc., Alibre, 2003. http://www.alibre.com/. [25] L.Y. Jae, K. Hyun, K. Kwangsoo, A web-enabled approach to feature-based modeling in a distributed and collaborative design environment, Concurrent Engineering: Research and Applications 9 (1) (2001). [26] P. Rodgers, N.H. Caldwell, P.J. Clarkson, A. Huxor, The management of concept design knowledge in modern product organizations, International Journal of Computer Integrated Manufacturing 14 (1) (2001) 108–115. [27] J. Shi, G.Q. Huang, K.L. Mak, C.H. He, Web-based ‘‘design for X’’ for collaborative product development, in: Proceedings of the International Conference on Concurrent Engineering, California, USA, 2001. [28] M.N. Latif, R.G. Hannam, Feature-based design and the object-oriented approach, Journal of Engineering Design 7 (1) (1996) 27–37. [29] A. Al-Ashaab, R. Young, Design for injection moulding in a manufacturing model environment, Journal of Design and Manufacturing 5 (1995) 45–54. [30] Project Management Institute, Guide to the Project Management Body of Knowledge, A (Pmbok1(Guide), 2000. [31] Progress Software, Objectstore, 2003. http://www.objectstore.net/index.ssp. [32] OMG (Object Management Group), Corba Basics, 2003. http://www.omg.org/gettingstarted/corbafaq.htm. [33] Sun Microsystems, The Source for Java Technology, 2003. http://java.sun.com/. [34] Microsoft Corporation, Windows Netmeeting, 1996–2000.
Ahmed Al-Ashaab is a Senior Lecturer in the School of Engineering and Built Environment in the University of Wolverhampton. Ahmed obtained his Ph.D. from Loughborough University in 1994. Since then he has worked in the ITESM Campus Monterrey in Mexico where 50% of his time was spent working with Mexican industry. He has been active in introducing and implementing NPI/D methodologies based on concurrent engineering within the Mexican manufacturing companies. He is the Founder and was the President of the Mexican Society of Concurrent Engineering. His research interests are CE, knowledge based engineering, extended and virtual enterprises and collaborative product development. Dr. Al-Ashaab has written many international publications and participated in several of conference committees and session chair. Dr. AlAshaab is the Publicity Chair of the ISPE/CE2xxx series conferences. Karina Rodriguez is Ph.D. student in the School of Engineering and Built Environment in the Wolverhampton University. She has a Computer Science Honour degree from the ITESM Campus Monterrey in Mexico in 1999. She worked as Research Assistant in the CSIM of ITESM Campus Monterrey in the SPEED project. Her research interests are knowledge based engineering, information modelling and internet based collaborative product development.
