Internet-based intensive product design platform for ... - GRACO

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10 Table 2 Instanced information model of rolling bearing 6215 Instanced STEP information model Format ¼ ‘rose_r3.0’ ROSE_OIDS ( (0 ¼ 0 £ 0000000000000000000000000000000000000000) (1 ¼ 0 £ 01000004D20000387AD825000000B30000000000)) ROSE_DESIGN (RoseDesign name: bearing6215 root: $ keyword_table: $ name_table: $ schemas: (,1-1 . ListOfRoseDesign , “rolling_bearing_support_schema” . )) STEP_OBJECTS ( (,1-0 . bearing6215 style: “general ball bearing with deep groove” static_load: 66 dynamic_load: 49.5 weight: 1.171 limited_speed_with_oil: 5600 limited_speed_with_grease: 4500 external_diameter: 130 inner_diameter: 75 width: 25 install_damin: 0 install_damax: 0 install_rasmax: 0 other_d2_inner: 0 other_d2_external: 0 other_rsmin: 0)) is rolling_bearing_support_schema.rose). Populating the data dictionary means to endue an exact value into every parameter of the information model. Table 2 shows the instanced information model of the rolling bearing that populated the 6215 rolling bearing. Once the information model is instanced, all parameters of the information model are confirmed. The detailed structural data of the 6215 rolling bearing is shown in Fig. 2. The 6215 STEP information model, containing all of the information stated in Fig. 2, is shown in Table 2. It shows that, by contrast with Fig. 2. Traditional handbook form of rolling bearing 6215. S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 Fig. 3. Mechanism of C/S database. the traditional representation form of knowledge, the STEP information model is a digital form of knowledge representation that can be understood with the product design platform and directly imported into it. This form of product design has the advantage of increasing the speed of knowledge acquisition and utilization. 4. The knowledge storage and transference Because the IDPP is built over the Internet, its repositories should be constructed with a database that is of the C/S mechanism so that it is easy to achieve the connection with design platform within Internet environment. In this paper, a prototype of knowledge repository is built with an SQL server database (shown as Fig. 3). The connection between the SQL database and the IPDP server is achieved through the Open Database Connection (ODBC). Once a user logs into the repositories of the IPDP via Internet control, fills out the forms and sends in the requests, then the IPDP server will start up the Active Server Pages (ASP) scripts and the CGI program corresponding to the request. ASP scripts can retrieve data from the SQL database through the database control ActiveX Data Object (ADO) inside the ASP, and return the results to the user. By this means, once the product designer has a need for knowledge that does not exist in his design platform, he can log into the IPDP server, search and retrieve knowledge according to his demands and the constraints that that knowledge should meet. 5. The method of knowledge search The common repositories of IPDP contain a great deal of knowledge that belongs to various disciplines and is distributed in various sites. When a designer needs to retrieve the knowledge from repositories of the IPDP so as to service for his product design, the efficiency of the method of knowledge search greatly affects the running performance of the IPDP. Therefore, the IPDP should provide a high performance method for knowledge search so as to retrieve knowledge quickly, exactly and efficiently
Fig. 4. The flowchart of searching support part on function driven. from its repositories. To this purpose, a method of knowledge search based on a function driven concept and an Artificial Neural Networks (ANN) technique is proposed in this paper. 5.1. Function driven Designing and developing a product is system engineering; it refers to multi-discipline knowledge. Even products with a single function or certain phases within the development of a product require the support of various kinds of knowledge. A designer could not master all of the necessary knowledge, or at least could not reach an expert’s level in all those disciplines. Certainly, he could not employ a knowledge that he does not completely know. This means that much knowledge may be familiar to, but not mastered by the designer though he may know the function and constraints of this knowledge. Based on this viewpoint, the IPDP proposes a method of knowledge search. That is, the knowledge is searched according to the functions of the knowledge, here called Function Driven Knowledge Search (FDKS). With this method, when a designer needs a piece of knowledge he determines the function of the knowledge and the constraints that the knowledge requires. Then the IPDP searches for the knowledge in the repositories and retrieves it, matching the required functions and constraints. For example, suppose that a designer needs a rotative support part when he develops a product, and the part does not exist inthedatabaseofhisdesignplatform.Hewantstogain it from the outside repositories. Obviously, he does not know the details of the part. But he knows the functions of the part and the constraints that the part should meet. Since the IPDP provides the (FDKS) method, the designer only needs to provide the function and constraints that should be met. The IPDP then returns to the designer the resulting part that coincides with the user’s requirements. By this means, the designer can gain the knowledge that meets his design requirements even if he is not very familiar with it. The flowchart of S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 11 searching the support part based on function driven knowledge is shown in Fig. 4. 5.2. Back propagation neural networks Knowledge search driven by function requires the user to determine the function and constraints that the knowledge should meet. In many situations, a product (knowledge) has several functions, and the same function can be achieved with various products (knowledge). Moreover, the repositories have a great deal of knowledge and the mapping relationships between function and knowledge are implicit and nonlinear. In these situations, it is very difficult to construct all mapping relationships through general mapping (e.g. rule reasoning). Then how does the IPDP efficiently implement the mapping between knowledge and the function of knowledge? To solve this problem, an ANN method is applied to construct the mapping relationship between functions and knowledge in the IPDP system. In 1986, D.E. Rumelhart, J.L. Meclelland of MIT University put forward a Back Propagation (BP) algorithm of multi-layers feed forward ANN. The BP algorithm solved the modeling problem for layer-networks, and realized the ANN used in engineering applications. Until the present the synapses of ANN have almost reached all fields of engineering application, such as machine vision, class of decision [12], intelligent control and fault diagnosis [13]. Since the feed forward neural networks with two layers (containing a hidden layer) were applied in class and curve approximate fitting, a two-layer BP neural network with a momentum coefficient is adopted here to construct the mapping relationships between functions of knowledge and knowledge itself. The mathematical model of BP neural networks is shown as follows. Training samples are {ðXp; DpÞlp ¼ 1; 2; …; N}: Here, Xp is the Pth input value of training samples. Xp ¼ ðx p p 1 ; x2 ; …x p a Þ: Dp is the Pth ideal output value of training samples, Dp ¼ðd p p p 1 ; d2 ; …dL Þ: Yp is the Pth actual output value, Yp ¼ðy p p p 1 ; y2 ; …yL Þ: To the Pth group of training samples with N groups, the input and output models of the hidden layer are stated as Eq. (1). The input and output model of the output layer are stated as Eq. (2). net p XM j ¼ Wijx i¼0 p i o p 8 >< ð1Þ >: p j ¼ f netj 8 >< >: net p k y p k ¼ XS j¼0 ¼ f net p k W jko p j Select Sigmoid function f ðuÞ ¼1=½1 þ expð2uÞŠ as the active function of the BP neural networks. The valve values ð2Þ
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Page 3: Table 1 EXPRESS information model o
Page 7 and 8: the BP network is shown as Fig. 5.
Page 9: Acknowledgments This research was p

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