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Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 560856, 6 pages
Knowledge Flow Rules of Modern Design under Distributed Resource Environment
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
Received 20 November 2012; Accepted 4 March 2013
Academic Editor: Shandong Tu
Copyright © 2013 Junning Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The process of modern design under the distributed resource environment is interpreted as the process of knowledge flow and integration. As the acquisition of new knowledge strongly depends on resources, knowledge flow can be influenced by technical, economic, and social relation factors, and so forth. In order to achieve greater efficiency of knowledge flow and make the product more competitive, the root causes of the above factors should be acquired first. In this paper, the authors attempt to reveal the nature of design knowledge flow from the perspectives of fluid dynamics and energy. The knowledge field effect and knowledge agglomeration effect are analyzed, respectively, in which the knowledge field effect model considering single task node and the single knowledge energy model in the knowledge flow are established, then the general expression of knowledge energy conservation with consideration of the kinetic energy and potential energy of knowledge is built. Then, the knowledge flow rules and their influential factors including complete transfer and incomplete transfer of design knowledge are studied. Finally, the coupling knowledge flows in the knowledge service platform for modern design are analyzed to certify the feasibility of the research work.
As an intangible asset of the enterprises and the whole human, knowledge is very important for human survival and development, especially in an era of knowledge. Recently, people pay more and more attention to the research of the acquisition, discovery, publishing, transmittal, and integration of knowledge [1–8]. The results of these works can be able to transform their unique advantages into profits for economic growth and social progress. People have made many studies in theory and application of knowledge. Zhang and Yue studied the welding technology of knowledge in the aspect of knowledge representation, use, and acquisition . Umapathy and Purao studied the mechanisms of design knowledge description and acquisition based on the service integration mode . Nissen combined workflow and knowledge management technology; he put forward an expanding 4-dimensional dynamic knowledge flow model, which can achieve effective integration of knowledge flow and work flow .
The increasingly fierce market competition and user requirements present higher request for product development than before. In order to win market opportunities, we should make full use of distributed knowledge to design a more competitive product efficiently. Along with the improvement of product performance, modern product design has presented a system features obviously. Knowledge integration is a crucial part of the system integration design, so the integrated design of large and high-performance equipment needs more knowledge and knowledge acquisition ability than part design. The knowledge flow among members involved in the design whose efficiency and quality decides the design in the distributed resource environment. If knowledge cannot flow or flow poorly, design activities also cannot or are inefficient. We should understand knowledge flow rules in the integration design deeply so that we can make the best use of knowledge flow by controlling it consciously. Xie proposed knowledge flow theory, which regards the design process as a process of knowledge integration and flow among the various participants [11, 12]. In this theory, knowledge flow is decomposed into four levels of flow; each level of whom has its own characteristics and the results of the flow are to produce a competitive product. Under this theoretical system, Xie and his team had done a great deal of research, such as the important concepts of knowledge unit, design entity, performance, constrain, feature, demand driven and state, the acquisition, transmission, processing and application method of product data, the extraction and characterization method of product performance characteristic, the integration and sharing theory of distributed design resource, and full life cycle design for product design.
The study on knowledge covers a large field research, and the design knowledge is a subset of it. Most of previous studies focus on design knowledge management, knowledge representation, and knowledge modelling and their concern is on knowledge reuse rather than knowledge representation and modelling of design knowledge flow [13–15]. Aiming to further study knowledge representation and modelling, we should study knowledge flow rules firstly, such as design knowledge and its classification, knowledge acquiring method, and driving force of flow. There has been plenty of research in definition and classification of designing knowledge, so we focus on summary and analysis of knowledge flow rules and their influencing factors in this paper. Then we attempt to reveal the nature of design knowledge flow from the perspectives of fluid dynamics and energy.
2. Basic Terms
2.1. Knowledge Resource
Modern design strongly depends on resources. In this paper we define a knowledge resource as human knowledge in the design system, which can be used to promote the material production and thus create value or serve directly as a spiritual consumption. With the fast development of Internet, the knowledge resources display distributed characteristics obviously. We define the smallest unit in a field of knowledge as a knowledge unit, and the smallest unit can provide a knowledge resource or knowledge acquisition resource.
2.2. Knowledge Store
The knowledge store (KS) is a dynamic set of knowledge resources total at a certain phase one system, and its change is caused by knowledge flow and system structure under the level of macroscopic and microscopic flow. KS is a nonnegative value and can be expressed as:
In formula (1), the , , , and represent input variable, output variable, structure variable, and time. .
KS has a historical accumulation effect, which is a time-dependent increasing function without external interference. Here,
However, with strong interference by some external, such as an obstacle or termination of the design process by man-made, will decrease rapidly and its function will discontinue.
2.3. Knowledge Potential and Knowledge Potential Difference
Owing to the unbalanced distribution of knowledge in regional and professional, the among participants is different from each other, thus forming knowledge potential—call it . Knowledge potential is a strategic position which refers to continuous competition of participants by means of one or a little of knowledge in the knowledge flow field. Formula (3) is follows:
In formula (3), the , , and represent the main variable, knowledge property variable and time. Here, . From formula (3), we can see that the knowledge potential represents the degree of advantage for a specific subject in the certain knowledge. Its description includes a variety of types in many fields and we should regard knowledge state of the knowledge owner as a face not a point.
The knowledge potential difference—call it —is the difference between the high and low knowledge potential of the entities in one specific knowledge, which reflects the actual knowledge gap between knowledge entities. It always tends to be reduced. Formula is as follows (4):
2.4. Knowledge Flow
Modern design is started from need, and there is a demand potential difference between the design entity and resources requested. The difference led to the knowledge flow. Knowledge flow, as a dynamic variable, refers to the amount of knowledge resources flowing in and out of the system in one moment and it can be divided into inflows and outflows. Knowledge resources tend to flow toward a place of high demand potential or low knowledge potential. The flow process may also be associated with the material flow. If the inflows and outflows of knowledge keep balanced and it does not to generate knowledge in a system, the total value of the knowledge store remains unchanged, so this will achieve the dynamic balance of the system.
3. Knowledge Field Effect and Knowledge Agglomeration Effect
With the increasing awareness of the importance of knowledge and knowledge management, researchers have recognized that there is field effect in the process of knowledge creation, organization and sharing, and so forth [14, 16]. They put forward the concept of a knowledge field by introducing the concept and method of the field into knowledge management. Through analysis of the essential factors of discipline knowledge, the author thought that the smallest unit of discipline knowledge is the foundation of the knowledge field, and then he discussed the knowledge field effect with examples . However, the related research mostly focuses on the field of existing knowledge and knowledge management. The study of He Rong-li is close to our topic, whereas he focuses on the diffusion and absorption of discipline knowledge to describe the process of knowledge crossing and reorganization, and the result of knowledge flow is to create a new discipline. However, one important work of modern design is knowledge acquisition, so we should acquire new knowledge as possible in the knowledge flow process. Furthermore, because the knowledge need is a dynamic set, knowledge flow shows a more complex state and the dynamics of the knowledge field effect are more obvious, and the result of knowledge flow is creation of a new product.
Knowledge field effect corresponds to the knowledge agglomeration effect, while the emergence and development of a knowledge field must be accompanied with the knowledge agglomeration phenomenon. The knowledge store approach is a level that forms the knowledge potential difference with which to promote knowledge flow. The knowledge potential difference tends to decrease with the knowledge flow until it stopped. The next, knowledge flow come again with a new knowledge potential difference, then it will be done again and again until the whole design process is completed. The knowledge flow is restricted by many factors such as technical, economic, and social restraints, which are usually in nonideal state. There are also some specific constraints in every stage of the knowledge flow, so the knowledge flow is completed by the combination of drive and restrictive. The flow is laminar fluid flow as a whole, accompanied by a certain amount of the eddy and turbulence.
The main analysis of this paper focuses on the dynamic effects in the process of knowledge flow. For a specific design task, the knowledge field effect model is showed in Figure 1.
In Figure 1, B as a dynamic knowledge absorption field, which receives first, then it analyzes and solves it with the combined action of A, C, and D, at last the solution is output. As a dynamic node in the design process, the result of generally is used as input to the next design task such as . Design nodes, as the smallest knowledge gene in the design process, they can constitute the whole design process by integration effectively. The A, C, and D are collectively known as the design knowledge, which is a general concept and comes from a variety of discipline knowledge and domain knowledge. The A, C, and D are known as the knowledge diffusion field in the knowledge field effect model, according to the specific design task, they are units with high knowledge potential, which can flow to the knowledge absorption field under the action of a potential difference and to complete the design task together with a design entity. Due to the objective and man-made reasons, the dissipations of knowledge are unable to avoid, expressed as E and F.
The arrows represent force lines of knowledge, while the lines of knowledge absorption field and knowledge diffusion field both center on the knowledge gene. The more forceful lines exist, the greater the field intensity associated with them is, so knowledge flow is achievable more easily.
With the deep analysis of knowledge field effect and the knowledge agglomeration effect, we can control the knowledge flow to a certain extent, and then we also can optimize correlative factors to promote knowledge flow.
4. Knowledge Flow Rules under the Perspective of Energy
Nowadays, as the competition is fierce and complicated, the design depends on knowledge in distributed resources environment more and more. As a consequence, the knowledge flow occurs frequently, and it is more obvious and stronger than ever, so the deep analysis of knowledge flow rules in modern design is essential.
To some extent, design knowledge flow is a kind of energy flow, whose flow rules also meet the energy conservation law. In this paper, we view knowledge kinetic energy as one that occurs in the knowledge transfer process, which is related to the generalized mass and velocity. Here, we have the following hypothesis:
In formula (5), is concerned with knowledge property and represents knowledge flow speed. It shows that the process of knowledge flow is an association with knowledge property, knowledge flow speed, and other constraints. Consider the actual situation of knowledge flow, we should do further decomposition on and as follows:
In formula (6), the , , , , and represent function, quality, promotion factor, restrictive factors, and time. It shows that is an association with function, quality, and time, while is the association with the promotion factor, restrictive factors, and time.
The knowledge flow rules under the perspective of energy are that the knowledge flow from the high-energy unit to low-energy unit and the total energy keeps dynamic balance in the flow process for a specific knowledge objects which directly involved in this flow. Figure 2 shows the single knowledge energy model. The and represent the knowledge provider and knowledge receiver, and the knowledge potential difference between and is knowledgeable energy, namely, . As a basic unit, this model can be extended to the modeling and analysis of the whole process of knowledge flow.
The total energy keeps dynamic balance in the system composed of and , as the following formula (7):
Furthermore, the system can be divided into two kinds of cases according to the special flow process.(1)Knowledge is completely transferred between two entities. In this case, at last, , . It is a perfect condition for knowledge flows fully.(2)Knowledge is not completely transferred between two entities. At last, , . It is a restriction condition for knowledge flow.
The , , and represent the environment, the effect on knowledge dissipation by knowledge provider, the effect on knowledge dissipation by knowledge receiver and time.
5. Analysis and Discussion
The knowledge acquisition in the selection of rolling bearing is taken as an example. It contains five tasks that are the selection of bearing type, calculation of bearing parameters, determination of bearing type, analysis of bearing performances, and determination of bearing details, which are also the subtask in the entire design of rolling bearing.
In Figure 3, – represents design task, which can be divided into some subtasks. For specific task, , . – represents input knowledge unit, which contains knowledge unit A, C, and environmental knowledge D. O11–O14 represents output knowledge, which also can be viewed as the next design task. Knowledge loss and knowledge transfer are ascribed to the contradiction between available knowledge and all the knowledge we can obtain. Product designers represent design entity B.
The entire design can be finished by design entity integrating with knowledge unit effectively in the process of knowledge flow. The total energy keeps dynamic balance in the process of design and knowledge flow.
To achieve the integration of distributed knowledge in distributed resource environment, we should develop the resource integration system engine and resource pool to form an integrated knowledge flow net, which can help the user to search for the knowledge they need quickly in the vast network of knowledge. According to the idea of “a portal with multiple sites,” we cooperate with related websites using UDDI to build the integrated knowledge service platform for modern design. The platform aims to improve the competitiveness of China’s manufacturing enterprises in product design and innovation, so we should focus on the overall planning and construction of it through the user perspective. On the basis of knowledge classification, we study the operating mechanism of various coupling knowledge flows in the platform, and then its topological relations are shown in Figure 4.
The knowledge service platform for modern design is a knowledge cluster platform, which has various distributed design knowledge resources. It has the knowledge field effect and the knowledge agglomeration effects obvious which can promote the knowledge flow among the participants. Restricted by various objective and subjective conditions, the flow is a limited knowledge flow and tends to reduce the knowledge potential difference until we complete design.
From the perspective of knowledge potential and knowledge potential difference, the entities in the platform have demand potential, while the platform has the knowledge energy, so there is certain knowledge potential difference between them. Under the action of it forms knowledge flow to solve the special design task.
With the knowledge agglomeration effect, the platform has a high knowledge store and knowledge potential. Driven by the knowledge potential, it forms a certain knowledge flow in the platform. The higher the knowledge store, the greater the knowledge flow and competitiveness of website are.
It can be found in the total knowledge energy that keeps dynamic balance in the flow process, so is the Macro process. According to a specific design process, the knowledge energy also keeps dynamic balance between knowledge provider and knowledge receiver.
Modern design is knowledge based, on new knowledge acquirement centered and distributed resource environment depended. The process of modern design is interpreted as the process of knowledge flow and integration. As the acquisition of new knowledge strongly depends on resources, knowledge flows may be influenced by technical, economic, and social relation factors, and so on. In order to achieve a greater efficiency of knowledge flow and make the product more competitive, we should understand the root causes of these factors first.(1)The basic concepts such as knowledge resource, knowledge store, knowledge potential, knowledge potential difference, and knowledge flows are introduced. With them, we analyze the dynamic characteristics of the design knowledge, the driving force, constraints and control elements of the flow, and other factors, which can create a condition for modeling and analysis of knowledge flow.(2)The concept of a knowledge field is put forward, then the knowledge field effect and the knowledge agglomeration effects are analyzed, respectively; the knowledge field effect model of the single task node is established, and the general expression of knowledge energy conservation with consideration of the kinetic energy and potential energy of knowledge is built.(3)The knowledge flow rules under the perspective of energy are studied, and the general expression of knowledge energy conservation with consideration of the kinetic energy and potential energy of knowledge is built. Then, the knowledge flow rules and their influential factors including complete transfer and incomplete transfer of design knowledge are studied.(4)Finally, the coupling knowledge flows in the knowledge service platform are analyzed to certify the feasibility of the research work in this paper. This study can be used as the basis for further study on knowledge flows theory. The future work is to achieve it completely.
This research was supported by National Natural Science Foundation of China under Grant no. 59990470.
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