# Double Push Strategy of Knowledge for Product Design Based on Complex Network Theory

Authors: Xue-rui Li; Sui-huai Yu; Jian-jie Chu; Deng-kai Chen; Lin-jian Wu
Journal: Discrete Dynamics in Nature and Society
 (2017)

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## Abstract

Reasonable application of design knowledge can help improve the efficiency and quality of product design. Based on complex network theory, this study proposes a double push strategy of knowledge for product design. The proposal introduces the concept of attribute similarity and triangular fuzzy number and uses the theory and method of complex network to build the knowledge network model for product design that contains creative knowledge subnetwork and engineering knowledge subnetwork. This paper is to understand the structure and dynamics of the knowledge network model and to identify and predict knowledge nodes and knowledge groups strongly related to design intent in view of the scale-free network topology analysis theory. We develop a double push strategy of product design knowledge to implement the effective auxiliary function for product design process. Finally, a design case of antalgic pump is presented to demonstrate the practicability and validity of the strategy.


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## Body

## 1. Introduction

Essentially, the product design is a process that designers and engineers use their design knowledge and experience to solve problems on the basis of demand analysis. The process is based on multidomain knowledge creation activities and aimed at building multiple qualities for products, services, and systems that constitute the entire life cycle. However, with the development of information technology, the design process has become extremely complex because of the complexity of object of product. The design process of modern products has evolved into a continuous and complex cognitive process that covers such diverse fields as aesthetics, psychology, behavioral cognition, biology, mechanical engineering, and human engineering. This change requires designers to begin to control the whole product design cycle in the early stage of design process.The product design process which is the most creative and important stage during the product life cycle determines the final level of product innovation and market success rate. However, a survey [1] shows that designers spend the majority of their time managing design knowledge and less time doing design and analytic work where they can use their specialized expertise to create the greatest value for projects; the results of the survey are shown in Figure 1.Figure 1
The proportion of time spent by designers in design process.With increasing complexity in design process, it needs optimization team and broad interdisciplinary knowledge to support the creation of designer creative scheme in the design process. It is necessary to rely on accurate intelligent push of knowledge to assist designers in their creative work rather than relying solely on the designer’s independent behavior. The double push strategy of knowledge for product design is proposed which aims to assist designers in innovative behavior and design process rather than automation of innovative design.The remainder of this paper is organized as follows. In Section2, the related works on knowledge management and push are discussed. Our method for a double push strategy of knowledge is presented in Sections 3, 4, and 5: in Section 3 we firstly construct knowledge model for product design on ontology; in Section 4 we build a knowledge network model for product design; in Section 5 we propose a double oriented push strategy of product design knowledge based complex network theory. Section 6 reports the implementation of the method and shows examples about knowledge push for the product design process of analgesic pump. The conclusion is drawn in Section 7.


## 2. Related Work

Designers need to acquire a great deal of knowledge during the product design process. Effective management and push of knowledge can help to stimulate innovative thinking, enhance innovation efficiency, and shorten design cycle time [2]. At present, the research of knowledge management theory and method is developing rapidly, and it begins to extend from the economic and management fields to other research and application fields [3]. However, because of involving a number of different areas, including aesthetics, psychology, mechanical processing, and man-machine engineering, design knowledge belongs to complex and diverse unstructured knowledge, which has become a major challenge for knowledge management and push in the design process. Some scholars have begun to study the knowledge management and push method in the design process. In 2011, Park developed a knowledge management system for storing and using the design knowledge acquired in the process of a user-centered design of the next generation information appliances [4]. In 2016, Jansson et al. examined how designers’ unique knowledge backgrounds can fuel analogy-based creativity which can support claims from the knowledge-sharing literature for a direct route from knowledge diversity through analogical reasoning to novel idea production [5]. In 2013, Matta et al. proposed a new technique to acquire and represent reusable design knowledge in a design project memory using product lifecycle management platforms [6]. In 2016, Feng et al. proposed an intelligent push method of CNC design knowledge based on latent semantic analysis aiming at the design knowledge demand difference of different designers in CNC design knowledge push process driven by design procedure [7]. In 2015, Liang et al. proposed a dynamic design knowledge push technology based on variable-weight layered spreading activation model to achieve precision and dynamics of product design knowledge push [8]. In 2015, Wang et al. present a new knowledge push technology for complex mechatronic products design based on ontology and variable precision rough set [9]. The diversity and unstructured attributes of design knowledge determine the difficulty of knowledge management and push in product design process. However, the existing methods focus on the representation, modeling, and reuse of design knowledge. Most of these researches are more inclined to develop general knowledge service technology.Complex network theory mainly uses graph theory and statistical physics to study the topological structure and properties of networks. The discovery of small world networks [10] and scale-free networks [11] has promoted the rapid development of complex networks. At present, there are still few application researches about complex network theory in product design. The theory now mainly focuses on complex mechanical product system, such as network characteristic demonstration [12, 13], design process management [14, 15], product family design [16], and modular design [17, 18]. Some scholars have started applying complex network theory to knowledge management of mechanical design; for example, Yang et al. proposed a construction method of mechanical design knowledge network based on complex network [19]. However, there is not a yet mature application about complex network theory in product design.In order to better support product design, this paper proposes a double push method of knowledge for product design based on complex network theory which can achieve more accurate and dynamic knowledge push in product design process.


## 3. Product Design Knowledge Modeling

### 3.1. Product Design Knowledge

Product design knowledge refers to a set of information accumulated from design theory and work experience to support designers in creative design, structural design, and engineering design. The field of product design knowledge covers aesthetics, psychology, behavioral cognition, biology, mechanical engineering, and ergonomics, which belongs to complex and diverse unstructured knowledge. In product design process, a large number of texts, images, videos, 3D digital models, virtual scenes, and other unstructured information can be used to enrich the designer’s thinking space, so as to stimulate new and feasible design concepts. At the same time, materials processing, mechanical engineering, packaging technology, ergonomics, and other knowledge can be used to assist in the completion of the design program. According to the product design process, the design knowledge domain is divided into the creative knowledge domain and the engineering knowledge domain (Figure2).Figure 2
Knowledge domain partitioning model based on product design process.(i) Creative Knowledge Domain. The creative knowledge domain stores heuristic abstract knowledge to stimulate creative thinking of designers through visual stimulation. It mainly involves unstructured knowledge such as creative resources, family products, and Pan-Ethnic-Group products [2].(ii) Engineering Knowledge Domain. The engineering knowledge domain stores the specific knowledge in the different fields of the design and implementation process to assist the implementation of the design scheme. It mainly involves structured knowledge such as patents, technical standards, materials, processing technology, and other engineering knowledge.


### 3.2. Product Design Knowledge Modeling

In order to improve the utilization of design knowledge and actualize the reuse of knowledge to support the innovation design, this paper constructs the product design knowledge model based on ontology and stores the structured knowledge effectively. Because of the difference of knowledge attributes between creative knowledge domain and engineering knowledge domain, this paper classifies product design knowledge during the following research.


#### 3.2.1. Structured Description of the Creative Knowledge Domain

In form, the creative knowledge can be expressed in six-tuple modeling as follows:(1)KC=KCid,KCt,KCa,KCk,KCi,KCe.The meaning of each symbol is as follows:KC: knowledge in the creative knowledge domain; KCid: identification of knowledge; KCt: the type of knowledge; KCa: application phase of knowledge; KCk: Kansei image (in the previous studies, the author has used questionnaires and cluster analysis to extract the 16 Kansei imagery words that best represent the designer’s intention in the creative design process [20]); KCi: the importance of knowledge in its domain; KCe: the expression of knowledge, including sketch, image, model, and video.


#### 3.2.2. Structured Description of the Engineering Knowledge Domain

In form, the engineering knowledge can be expressed in six-tuple modeling as follows:(2)KE=KEid,KEt,KEa,KEf,KEi,KEe.The meaning of each symbol is as follows:KE: knowledge in the engineering knowledge domain; KEid: identification of knowledge; KEt: the type of knowledge; KEa: application phase of knowledge; KEf: the field of knowledge, including material, processing technology, and structure; KEi: significance degree of knowledge, that is, the importance of knowledge in its domain; KEe: the expression of knowledge, including data, drawing, manual, and model.Using the above methods, the product design knowledge base is constructed which includes creative knowledge domain and engineering knowledge domain.


## 3.1. Product Design Knowledge

Product design knowledge refers to a set of information accumulated from design theory and work experience to support designers in creative design, structural design, and engineering design. The field of product design knowledge covers aesthetics, psychology, behavioral cognition, biology, mechanical engineering, and ergonomics, which belongs to complex and diverse unstructured knowledge. In product design process, a large number of texts, images, videos, 3D digital models, virtual scenes, and other unstructured information can be used to enrich the designer’s thinking space, so as to stimulate new and feasible design concepts. At the same time, materials processing, mechanical engineering, packaging technology, ergonomics, and other knowledge can be used to assist in the completion of the design program. According to the product design process, the design knowledge domain is divided into the creative knowledge domain and the engineering knowledge domain (Figure2).Figure 2
Knowledge domain partitioning model based on product design process.(i) Creative Knowledge Domain. The creative knowledge domain stores heuristic abstract knowledge to stimulate creative thinking of designers through visual stimulation. It mainly involves unstructured knowledge such as creative resources, family products, and Pan-Ethnic-Group products [2].(ii) Engineering Knowledge Domain. The engineering knowledge domain stores the specific knowledge in the different fields of the design and implementation process to assist the implementation of the design scheme. It mainly involves structured knowledge such as patents, technical standards, materials, processing technology, and other engineering knowledge.


## 3.2. Product Design Knowledge Modeling

In order to improve the utilization of design knowledge and actualize the reuse of knowledge to support the innovation design, this paper constructs the product design knowledge model based on ontology and stores the structured knowledge effectively. Because of the difference of knowledge attributes between creative knowledge domain and engineering knowledge domain, this paper classifies product design knowledge during the following research.


### 3.2.1. Structured Description of the Creative Knowledge Domain

In form, the creative knowledge can be expressed in six-tuple modeling as follows:(1)KC=KCid,KCt,KCa,KCk,KCi,KCe.The meaning of each symbol is as follows:KC: knowledge in the creative knowledge domain; KCid: identification of knowledge; KCt: the type of knowledge; KCa: application phase of knowledge; KCk: Kansei image (in the previous studies, the author has used questionnaires and cluster analysis to extract the 16 Kansei imagery words that best represent the designer’s intention in the creative design process [20]); KCi: the importance of knowledge in its domain; KCe: the expression of knowledge, including sketch, image, model, and video.


### 3.2.2. Structured Description of the Engineering Knowledge Domain

In form, the engineering knowledge can be expressed in six-tuple modeling as follows:(2)KE=KEid,KEt,KEa,KEf,KEi,KEe.The meaning of each symbol is as follows:KE: knowledge in the engineering knowledge domain; KEid: identification of knowledge; KEt: the type of knowledge; KEa: application phase of knowledge; KEf: the field of knowledge, including material, processing technology, and structure; KEi: significance degree of knowledge, that is, the importance of knowledge in its domain; KEe: the expression of knowledge, including data, drawing, manual, and model.Using the above methods, the product design knowledge base is constructed which includes creative knowledge domain and engineering knowledge domain.


## 3.2.1. Structured Description of the Creative Knowledge Domain

In form, the creative knowledge can be expressed in six-tuple modeling as follows:(1)KC=KCid,KCt,KCa,KCk,KCi,KCe.The meaning of each symbol is as follows:KC: knowledge in the creative knowledge domain; KCid: identification of knowledge; KCt: the type of knowledge; KCa: application phase of knowledge; KCk: Kansei image (in the previous studies, the author has used questionnaires and cluster analysis to extract the 16 Kansei imagery words that best represent the designer’s intention in the creative design process [20]); KCi: the importance of knowledge in its domain; KCe: the expression of knowledge, including sketch, image, model, and video.


## 3.2.2. Structured Description of the Engineering Knowledge Domain

In form, the engineering knowledge can be expressed in six-tuple modeling as follows:(2)KE=KEid,KEt,KEa,KEf,KEi,KEe.The meaning of each symbol is as follows:KE: knowledge in the engineering knowledge domain; KEid: identification of knowledge; KEt: the type of knowledge; KEa: application phase of knowledge; KEf: the field of knowledge, including material, processing technology, and structure; KEi: significance degree of knowledge, that is, the importance of knowledge in its domain; KEe: the expression of knowledge, including data, drawing, manual, and model.Using the above methods, the product design knowledge base is constructed which includes creative knowledge domain and engineering knowledge domain.


## 4. Knowledge Network Model for Product Design

The knowledge network model for product design (KN-model) proposed in this paper contains two independent subnetworks: the creative knowledge subnetwork based on the creative knowledge domain and the engineering knowledge subnetwork based on the engineering knowledge domain.The KN-model can be mathematically described as a graphG=N,E, where N=1,…,n is the set of all nodes in the graph and E⊆V×V is the set of edges between pairs of nodes in the network. At the same time, the graph G=G1,G2, where both G1 and G2 are subgraphs. The whole network connectivity is completely described by the symmetric adjacency matrix:(3)Aij=Aji=1,i,j∈E,0,i,j∉E.BothAij and Aji are the connection status between Ni and Nj:Aij=1 when link i,j exists; that is, there is a relationship between the two nodes; Aij=0 otherwise; that is, there is no relationship between the two nodes.


### 4.1. Fuzzy Importance Calculation

The type of knowledge and the division of knowledge domain depend heavily on the knowledge system administrators and designers. Some of knowledge is classified by administrators and some designers, and a sample library of knowledge types and knowledge domains is obtained. Thus, the feature words of each knowledge type and each knowledge domain are obtained, and the corresponding feature vectors are established according to the feature words. The feature vectorFvj of knowledge domain j consists of both the feature word and the weight of the feature word in knowledge domain j which can be modified as(4)Fvj=t1,wj1f,t2,wj2f,…,tn,wjnf,where n is the dimension of feature vector and wjnf is the weight of feature word tn in knowledge domain j.Fuzzy set theory is a mathematical theory to effectively analyze and deal with incertitude and incompleteness [21]. Triangular fuzzy number is a concept based on the basic principles of rough set theory, which can handle the vagueness and subjectivity.Definition 1.
Leta=aL,aM,aU be a triangular fuzzy number, where aL stands for the lower bound of value, aM stands for the most probable value, and aU stands for the upper bound of value. Then the membership function can be represented as(5)μAx=x-aLaM-aL,x∈aL,aM,x-aUaM-aU,x∈aM,aU,0,x∈-∞,aL∪aU,+∞.Definition 2.
Supposea=aL,aM,aU and b=bL,bM,bU are triangular fuzzy numbers. The operation of triangular fuzzy number is derived as (6)a+b=aL,aM,aU+bL,bM,bU=aL+bL,aM+bM,aU+bU,k×a=kaL,kaM,kaU,k≥0.The importance of knowledge is dynamic and fuzzy. In order to facilitate the measurement, we use triangular fuzzy number to characterize fuzzy judgment information. On this basis, this paper proposed a novel evaluation approach to assist in fuzzy importance calculation of knowledge nodes.Step 1 (establishing fuzzy group evaluation matrix).
Suppose there ares experts in the decision-making team and m knowledge domains Gi(i=1,2,…,m), and Gi contains knowledge nodes ki1,ki2,…,kin. Taking the subtask corresponding to Gi as the criterion and the knowledge node kij(j=1,…,n) of Gi as the subcriterion, the indirect dominance between each element in the element group Gi is compared according to its influence on kij; then the judgment matrix can be constructed:(7)A∗=c11∗c12∗⋯c1n∗c21∗c21∗⋯c2n∗⋮⋮⋱⋮cn2∗cn2∗⋯cnn∗,∗∈1,s,where cij∗=cij1,cij2,…,cijs is the triangular fuzzy number corresponding to the evaluation data of s experts in the decision-making team. cij∗ can be represented as(8)TFNcij∗=cij∗L,cij∗M,cij∗U,where cij∗L, cij∗M, cij∗U are the lower bound, most probable value, and upper bound of TFN(cij∗). Therefore,(9)TFNcij∗=cij1L,cij1M,cij1U,…,cijsL,cijsM,cijsU.
Using (6), the average triangular fuzzy number TFN(cij) can be got as(10)TFNcij=cijL,cijM,cijU,(11)cijL=cij1L+cij2L+⋯+cijsLs,cijM=cij1M+cij2M+⋯+cijsMs,cijU=cij1U+cij2U+⋯+cijsUs.
Therefore, we get the fuzzy group evaluation matrixA,(12)A=c11L,c11M,c11Uc12L,c12M,c12U⋯c1nL,c1nM,c1nUc21L,c21M,c21Uc22L,c22M,c22U⋯c2nL,c2nM,c2nU⋮⋮⋱⋮cn1L,cn1M,cn1Ucn2L,cn2M,cn2U⋯cnnL,cnnM,cnnU.Step 2 (calculating initial weights).
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By using (13), the initial weight of each knowledge node can be obtained as(14)D=D1L,D1M,D1UD2L,D2M,D2U⋮DnL,DnM,DnU.
The possibility of triangular fuzzy number can be applied to remove fuzziness,(15)pDi≥Dj=μd=1,Di≥Dj,DjL-DiUDiM-DiU+DjM-DjL,Di≤Dj,DiU≥DjL,0,otherwise.
Therefore, the initial weight of each knowledge node can be got as(16)R=R1,R2,…,Ri,…,Rn.Step 3 (calculating synthetical importance).
The importance of design knowledge is related to the weight of the knowledge itself and also to the number of times that the knowledge is retrieved. Therefore, the synthetical importance of design knowledgeKEi can be represented as(17)KEi=fRi+gMi∑i=1nMi,where Mi stands for the number of times that the knowledge node is retrieved and ∑i=1nMi stands for the number of times that all knowledge is retrieved. We introduce retrieval factor γ(0≤γ≤1) to transform the separation measures into crisp value:(18)KEi=γRi+1-γMi∑i=1nMi.
Therefore, we can rank all knowledge nodes according to their values ofKEi. By setting threshold KE0, we can identify the most important knowledge nodes in a knowledge domain. These knowledge nodes can be used to implement the first directional knowledge push.


### 4.2. Knowledge Network Modeling

According to the above network construction method, we know that the network is composed of nodes and edges, and the nodes of KN-model are product design knowledge. The key point of network construction is to determine the relationship among nodes, that is, the discovery of the edges. In this paper, we use attribute similarity to discover the relationship between the nodes of the product design knowledge network.The similarity formula of attribute similarity between knowledge points is established. The concrete calculation method is as follows.Suppose there are two knowledge nodesNi and Nj (containing M attributes), then the attribute similarity for attribute g between Ni and Nj is(19)asijg=xig-xg¯-xjg-xg¯.The meaning of each symbol is as follows:xig: the value of Ni about the attribute g; xjg: the value of Nj about the attribute g; xg¯: the average value of all knowledge points about the attribute g.In order to measure the similarity between two knowledge nodes, we need to standardize the attribute similarity.The specification of attribute similarity between the two knowledge nodes such asNi and Nj (containing M attributes) given by the upper segment is(20)ansi,j=∑n=1masijnm.Then,ansi,j is the attribute similarity between two knowledge nodes; ans0 is the threshold of attribute similarity. The smaller ansi,j is, the more similar two nodes will be. If ansi,j≤ans0, it can make clear the relation between the two product design knowledge nodes, then an edge can be created between two nodes. By calculating the attribute similarity between all nodes in KN-model, the relationships between the nodes in KN-model can be determined.


## 4.1. Fuzzy Importance Calculation

The type of knowledge and the division of knowledge domain depend heavily on the knowledge system administrators and designers. Some of knowledge is classified by administrators and some designers, and a sample library of knowledge types and knowledge domains is obtained. Thus, the feature words of each knowledge type and each knowledge domain are obtained, and the corresponding feature vectors are established according to the feature words. The feature vectorFvj of knowledge domain j consists of both the feature word and the weight of the feature word in knowledge domain j which can be modified as(4)Fvj=t1,wj1f,t2,wj2f,…,tn,wjnf,where n is the dimension of feature vector and wjnf is the weight of feature word tn in knowledge domain j.Fuzzy set theory is a mathematical theory to effectively analyze and deal with incertitude and incompleteness [21]. Triangular fuzzy number is a concept based on the basic principles of rough set theory, which can handle the vagueness and subjectivity.Definition 1.
Leta=aL,aM,aU be a triangular fuzzy number, where aL stands for the lower bound of value, aM stands for the most probable value, and aU stands for the upper bound of value. Then the membership function can be represented as(5)μAx=x-aLaM-aL,x∈aL,aM,x-aUaM-aU,x∈aM,aU,0,x∈-∞,aL∪aU,+∞.Definition 2.
Supposea=aL,aM,aU and b=bL,bM,bU are triangular fuzzy numbers. The operation of triangular fuzzy number is derived as (6)a+b=aL,aM,aU+bL,bM,bU=aL+bL,aM+bM,aU+bU,k×a=kaL,kaM,kaU,k≥0.The importance of knowledge is dynamic and fuzzy. In order to facilitate the measurement, we use triangular fuzzy number to characterize fuzzy judgment information. On this basis, this paper proposed a novel evaluation approach to assist in fuzzy importance calculation of knowledge nodes.Step 1 (establishing fuzzy group evaluation matrix).
Suppose there ares experts in the decision-making team and m knowledge domains Gi(i=1,2,…,m), and Gi contains knowledge nodes ki1,ki2,…,kin. Taking the subtask corresponding to Gi as the criterion and the knowledge node kij(j=1,…,n) of Gi as the subcriterion, the indirect dominance between each element in the element group Gi is compared according to its influence on kij; then the judgment matrix can be constructed:(7)A∗=c11∗c12∗⋯c1n∗c21∗c21∗⋯c2n∗⋮⋮⋱⋮cn2∗cn2∗⋯cnn∗,∗∈1,s,where cij∗=cij1,cij2,…,cijs is the triangular fuzzy number corresponding to the evaluation data of s experts in the decision-making team. cij∗ can be represented as(8)TFNcij∗=cij∗L,cij∗M,cij∗U,where cij∗L, cij∗M, cij∗U are the lower bound, most probable value, and upper bound of TFN(cij∗). Therefore,(9)TFNcij∗=cij1L,cij1M,cij1U,…,cijsL,cijsM,cijsU.
Using (6), the average triangular fuzzy number TFN(cij) can be got as(10)TFNcij=cijL,cijM,cijU,(11)cijL=cij1L+cij2L+⋯+cijsLs,cijM=cij1M+cij2M+⋯+cijsMs,cijU=cij1U+cij2U+⋯+cijsUs.
Therefore, we get the fuzzy group evaluation matrixA,(12)A=c11L,c11M,c11Uc12L,c12M,c12U⋯c1nL,c1nM,c1nUc21L,c21M,c21Uc22L,c22M,c22U⋯c2nL,c2nM,c2nU⋮⋮⋱⋮cn1L,cn1M,cn1Ucn2L,cn2M,cn2U⋯cnnL,cnnM,cnnU.Step 2 (calculating initial weights).
(13)
D
i
=
∑
j
=
1
n
c
i
j
∑
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=
1
n
∑
j
=
1
n
c
i
j
,
i
=
1,2
,
…
,
n
.
By using (13), the initial weight of each knowledge node can be obtained as(14)D=D1L,D1M,D1UD2L,D2M,D2U⋮DnL,DnM,DnU.
The possibility of triangular fuzzy number can be applied to remove fuzziness,(15)pDi≥Dj=μd=1,Di≥Dj,DjL-DiUDiM-DiU+DjM-DjL,Di≤Dj,DiU≥DjL,0,otherwise.
Therefore, the initial weight of each knowledge node can be got as(16)R=R1,R2,…,Ri,…,Rn.Step 3 (calculating synthetical importance).
The importance of design knowledge is related to the weight of the knowledge itself and also to the number of times that the knowledge is retrieved. Therefore, the synthetical importance of design knowledgeKEi can be represented as(17)KEi=fRi+gMi∑i=1nMi,where Mi stands for the number of times that the knowledge node is retrieved and ∑i=1nMi stands for the number of times that all knowledge is retrieved. We introduce retrieval factor γ(0≤γ≤1) to transform the separation measures into crisp value:(18)KEi=γRi+1-γMi∑i=1nMi.
Therefore, we can rank all knowledge nodes according to their values ofKEi. By setting threshold KE0, we can identify the most important knowledge nodes in a knowledge domain. These knowledge nodes can be used to implement the first directional knowledge push.


## 4.2. Knowledge Network Modeling

According to the above network construction method, we know that the network is composed of nodes and edges, and the nodes of KN-model are product design knowledge. The key point of network construction is to determine the relationship among nodes, that is, the discovery of the edges. In this paper, we use attribute similarity to discover the relationship between the nodes of the product design knowledge network.The similarity formula of attribute similarity between knowledge points is established. The concrete calculation method is as follows.Suppose there are two knowledge nodesNi and Nj (containing M attributes), then the attribute similarity for attribute g between Ni and Nj is(19)asijg=xig-xg¯-xjg-xg¯.The meaning of each symbol is as follows:xig: the value of Ni about the attribute g; xjg: the value of Nj about the attribute g; xg¯: the average value of all knowledge points about the attribute g.In order to measure the similarity between two knowledge nodes, we need to standardize the attribute similarity.The specification of attribute similarity between the two knowledge nodes such asNi and Nj (containing M attributes) given by the upper segment is(20)ansi,j=∑n=1masijnm.Then,ansi,j is the attribute similarity between two knowledge nodes; ans0 is the threshold of attribute similarity. The smaller ansi,j is, the more similar two nodes will be. If ansi,j≤ans0, it can make clear the relation between the two product design knowledge nodes, then an edge can be created between two nodes. By calculating the attribute similarity between all nodes in KN-model, the relationships between the nodes in KN-model can be determined.


## 5. A Double Oriented Push Strategy of Design Knowledge

### 5.1. Immunization Strategy in Complex Network

In order to enhance the novelty of product design knowledge and to stimulate innovation, immunization strategy can be used to immunize some of the nodes in the network. The study found that the acquaintance immunization strategy is effective for any broad-scale distributed network [22]. Figure 4 shows an improved acquaintance immunization strategy which is proposed in this paper and the immune process is as follows.Step 1.
Randomly selectX nodes from all knowledge nodes inKN-model.Step 2.
Determine whetherKi is confirmed. If confirmed, proceed to the next decision; if not confirmed, randomly immunize m neighboring nodes.Step 3 (ifKi≥Kcut).
Immunize nodeNi, and then randomly immunize a neighboring node of Ni.Step 4 (ifKi<Kcut).
First, randomly immunizemneighboring nodes; second, randomly selecta node (Nj) fromm nodes; finally, randomly immunizeaneighboring node of Ni.


### 5.2. Network Topology Analysis

#### 5.2.1. Importance Evaluation of Knowledge Nodes

The importance evaluation of knowledge nodes can help designers to extract knowledge nodes that are most closely related to a design intention so as to give priority to push and enhance the efficiency of innovation. The importance of a node is mainly expressed in the centrality of the node, that is, the number of nodes that are related to the node. In this paper, we adopt the concept of degree and degree distribution in complex networks to mine the most central nodes in the knowledge network.According to the concept of graph theory, thecumulative degree distribution function ofKN-model is calculated:(21)Pk=∑k′=k∞Pk′.It can be verified that if the node-degree distribution is a power law of the formPk~k-r with a constant r>0, then(22)Pk∝∑k′=k∞k′-r∝k-r-1.It can be proved that theKN-modelconstructed in Figure 3 is a scale-free network. Most of the knowledge nodes in the network are relatively low, but there are a small number of relatively high degrees of Hub nodes which are the knowledge nodes that most closely related to the design intention.Figure 3
The knowledge network model for product design (KN-model).Figure 4
The improved acquaintance immunization strategy.


#### 5.2.2. Identification of Knowledge Groups

In this paper, the cluster coefficient in the network topology is used as the object of analysis to identify and optimize knowledge clusters of the KN-model. The clustering coefficient can be used to describe the ratio of the adjacent nodes to the neighborhood of the nodes in the network, that is, the perfection of the small group structure. The cluster coefficients of the knowledge nodes in the above KN-MODEL can be defined as(23)CCi=3NΔiN3i,where NΔi is the total number of triangles containing the node Ni in the network(24)NΔi=∑k>jaijajkaik,where N3i is the total number of three tuples in the network that contain the node Ni(25)N3i=∑k>jaijaik.Obviously,0≤CCi≤1, and CCi=0 if and only if all neighbors are unconnected for any node in the network and CCi=1 if and only if all nodes are connected to each other.By setting a specific threshold, we can identify the nodes and their small group structure above the threshold, thus mining independent knowledge groups and providing visual design auxiliary information for the subsequent product design.


### 5.3. Double Push Strategy of Knowledge for Product Design

In order to achieve the goal of assisted innovation, this paper proposes a double push strategy of knowledge for product design (as seen in Figure5), and the knowledge push process is as follows.Figure 5
The double push strategy of knowledge for product design.Step 1.
The product design process is decomposed into two stages: creative design process and creative implement process, and the creative intention and engineering intention are fully excavated. And then, the product design knowledge base is constructed which includes creative knowledge domain and engineering knowledge domain.Step 2 (the first push).
Through the attribute similarity and triangular fuzzy number proposed in Section4.1, the knowledge nodes with the maximum weight value are identified, respectively. Then, the directional knowledge push for the first time can be in progress for the creative design process and creative implementation process.Step 3.
Through the acquaintance immune strategy proposed in Section5.1, some nodes inKN-modelare immune, so as to guarantee the novelty of the product design knowledge in the subsequent push link.Step 4 (the second push).
Through the calculation method of cluster coefficient in the network topology proposed in Section5.2, the knowledge nodes and the knowledge clusters related to creative intention and engineering intention are identified, respectively, then, the supplementary push for the second time can be in progress for the creative design process and creative implementation process.Through double push strategy of knowledge for product design, the design knowledge can be pushed accurately and directionally, and the aided innovation of design knowledge can be realized.


## 5.1. Immunization Strategy in Complex Network

In order to enhance the novelty of product design knowledge and to stimulate innovation, immunization strategy can be used to immunize some of the nodes in the network. The study found that the acquaintance immunization strategy is effective for any broad-scale distributed network [22]. Figure 4 shows an improved acquaintance immunization strategy which is proposed in this paper and the immune process is as follows.Step 1.
Randomly selectX nodes from all knowledge nodes inKN-model.Step 2.
Determine whetherKi is confirmed. If confirmed, proceed to the next decision; if not confirmed, randomly immunize m neighboring nodes.Step 3 (ifKi≥Kcut).
Immunize nodeNi, and then randomly immunize a neighboring node of Ni.Step 4 (ifKi<Kcut).
First, randomly immunizemneighboring nodes; second, randomly selecta node (Nj) fromm nodes; finally, randomly immunizeaneighboring node of Ni.


## 5.2. Network Topology Analysis

### 5.2.1. Importance Evaluation of Knowledge Nodes

The importance evaluation of knowledge nodes can help designers to extract knowledge nodes that are most closely related to a design intention so as to give priority to push and enhance the efficiency of innovation. The importance of a node is mainly expressed in the centrality of the node, that is, the number of nodes that are related to the node. In this paper, we adopt the concept of degree and degree distribution in complex networks to mine the most central nodes in the knowledge network.According to the concept of graph theory, thecumulative degree distribution function ofKN-model is calculated:(21)Pk=∑k′=k∞Pk′.It can be verified that if the node-degree distribution is a power law of the formPk~k-r with a constant r>0, then(22)Pk∝∑k′=k∞k′-r∝k-r-1.It can be proved that theKN-modelconstructed in Figure 3 is a scale-free network. Most of the knowledge nodes in the network are relatively low, but there are a small number of relatively high degrees of Hub nodes which are the knowledge nodes that most closely related to the design intention.Figure 3
The knowledge network model for product design (KN-model).Figure 4
The improved acquaintance immunization strategy.


### 5.2.2. Identification of Knowledge Groups

In this paper, the cluster coefficient in the network topology is used as the object of analysis to identify and optimize knowledge clusters of the KN-model. The clustering coefficient can be used to describe the ratio of the adjacent nodes to the neighborhood of the nodes in the network, that is, the perfection of the small group structure. The cluster coefficients of the knowledge nodes in the above KN-MODEL can be defined as(23)CCi=3NΔiN3i,where NΔi is the total number of triangles containing the node Ni in the network(24)NΔi=∑k>jaijajkaik,where N3i is the total number of three tuples in the network that contain the node Ni(25)N3i=∑k>jaijaik.Obviously,0≤CCi≤1, and CCi=0 if and only if all neighbors are unconnected for any node in the network and CCi=1 if and only if all nodes are connected to each other.By setting a specific threshold, we can identify the nodes and their small group structure above the threshold, thus mining independent knowledge groups and providing visual design auxiliary information for the subsequent product design.


## 5.2.1. Importance Evaluation of Knowledge Nodes

The importance evaluation of knowledge nodes can help designers to extract knowledge nodes that are most closely related to a design intention so as to give priority to push and enhance the efficiency of innovation. The importance of a node is mainly expressed in the centrality of the node, that is, the number of nodes that are related to the node. In this paper, we adopt the concept of degree and degree distribution in complex networks to mine the most central nodes in the knowledge network.According to the concept of graph theory, thecumulative degree distribution function ofKN-model is calculated:(21)Pk=∑k′=k∞Pk′.It can be verified that if the node-degree distribution is a power law of the formPk~k-r with a constant r>0, then(22)Pk∝∑k′=k∞k′-r∝k-r-1.It can be proved that theKN-modelconstructed in Figure 3 is a scale-free network. Most of the knowledge nodes in the network are relatively low, but there are a small number of relatively high degrees of Hub nodes which are the knowledge nodes that most closely related to the design intention.Figure 3
The knowledge network model for product design (KN-model).Figure 4
The improved acquaintance immunization strategy.


## 5.2.2. Identification of Knowledge Groups

In this paper, the cluster coefficient in the network topology is used as the object of analysis to identify and optimize knowledge clusters of the KN-model. The clustering coefficient can be used to describe the ratio of the adjacent nodes to the neighborhood of the nodes in the network, that is, the perfection of the small group structure. The cluster coefficients of the knowledge nodes in the above KN-MODEL can be defined as(23)CCi=3NΔiN3i,where NΔi is the total number of triangles containing the node Ni in the network(24)NΔi=∑k>jaijajkaik,where N3i is the total number of three tuples in the network that contain the node Ni(25)N3i=∑k>jaijaik.Obviously,0≤CCi≤1, and CCi=0 if and only if all neighbors are unconnected for any node in the network and CCi=1 if and only if all nodes are connected to each other.By setting a specific threshold, we can identify the nodes and their small group structure above the threshold, thus mining independent knowledge groups and providing visual design auxiliary information for the subsequent product design.


## 5.3. Double Push Strategy of Knowledge for Product Design

In order to achieve the goal of assisted innovation, this paper proposes a double push strategy of knowledge for product design (as seen in Figure5), and the knowledge push process is as follows.Figure 5
The double push strategy of knowledge for product design.Step 1.
The product design process is decomposed into two stages: creative design process and creative implement process, and the creative intention and engineering intention are fully excavated. And then, the product design knowledge base is constructed which includes creative knowledge domain and engineering knowledge domain.Step 2 (the first push).
Through the attribute similarity and triangular fuzzy number proposed in Section4.1, the knowledge nodes with the maximum weight value are identified, respectively. Then, the directional knowledge push for the first time can be in progress for the creative design process and creative implementation process.Step 3.
Through the acquaintance immune strategy proposed in Section5.1, some nodes inKN-modelare immune, so as to guarantee the novelty of the product design knowledge in the subsequent push link.Step 4 (the second push).
Through the calculation method of cluster coefficient in the network topology proposed in Section5.2, the knowledge nodes and the knowledge clusters related to creative intention and engineering intention are identified, respectively, then, the supplementary push for the second time can be in progress for the creative design process and creative implementation process.Through double push strategy of knowledge for product design, the design knowledge can be pushed accurately and directionally, and the aided innovation of design knowledge can be realized.


## 6. Implementation and Examples

The design process of antalgic pump is taken as an illustrative example to demonstrate the double push strategy of knowledge. The analgesic pump is a kind of digital and intelligent medical instrument, which is mainly used to achieve high precision infusion of liquid medicine in clinic through the intelligent make-up device such as motor drive and multichannel data acquisition. Therefore, the design of the analgesic pump belongs to a typical complex design problem.On the basis of detailed research and analysis of existing products, target users, and usage environment, the design is targeted at lightweight, and the original visual sense of medical equipment should be weakened so as to provide visual and psychological pleasure to the patients. Finally, the creative intention of the product design process is as follows: lively and elegant; the engineering intention is precise control, ease of use, stability, and maintainability.In this paper, the engineering intention “lightweight” is taken as an example to verify the effectiveness of the proposed push strategy; other design intents can be pushed in the same way.


### 6.1. Design Knowledge Modeling

Suppose there are 21 independent design knowledge items in the knowledge domain corresponding to engineering intent “lightweight.” Using the method proposed in Section3.2, 21 independent design knowledge items can be expressed in six-tuple modeling as follows:(26)KE=KEid,KEt,KEa,KEf,KEi,KEe.The meaning of each symbol is as follows:KE: knowledge in the engineering knowledge domain; KEid: identification of knowledge; KEt: the type of knowledge; KEa: application phase of knowledge; KEf: the field of knowledge, including material, processing technology, and structure; KEi: significance degree of knowledge, that is, the importance of knowledge in its domain; KEe: the expression of knowledge, including data, drawing, manual, and model. The structured description of every design knowledge is shown in Table 1.Table 1
Structured description of 21 design knowledge items.
K
E
i
d
K
E
t
K
E
a
K
E
f
K
E
i
K
E
e
K
1
Quality reduction rules
Design requirement
Design strategy
Mechanical engineering
Unknown
Data
K
2
Selection of light material
Source material
Material selection
Material science
Unknown
Manual
K
3
FEA model
Source material
Processing technology
Engineering and technology
Unknown
Model
K
4
CAD engineering analysis
Experience
Ergonomic design
Ergonomics
Unknown
Model
K
5
Parameter inversion
Definition
Structural design
Computer science
Unknown
Manual
K
6
Global optimization
Design requirement
Design thinking
Mechanical engineering
Unknown
Manual
K
7
Laminated structure (plate)
Source material
Structural design
Engineering and technology
Unknown
Drawing
K
8
Comparison of rigid plastics
Source material
Material selection
Material science
Unknown
Data
K
9
FEA model
Design principle
Structural design
Ergonomics
Unknown
Model
K
10
CAD strength calculation
Experience
Structural design
Engineering mechanics
Unknown
Drawing
K
11
Quality reduction rules
Design principle
Processing technology
Material science
Unknown
Manual
K
12
Machining condition restriction
Experience
Material selection
Mechanical engineering
Unknown
Manual
K
13
Selection of composite material
Source material
Material selection
Material science
Unknown
Manual
K
14
Hollow structure rule
Design principle
Structural design
Engineering mechanics
Unknown
Data
K
15
Computer simulation analysis
Design principle
Ergonomic design
Computer science
Unknown
Manual
K
16
Laminated structure (shell)
Source material
Structural design
Engineering and technology
Unknown
Drawing
K
17
Production management standards
Design requirement
Design strategy
Management
Unknown
Manual
K
18
Plastic cost manual
Source material
Material selection
Management
Unknown
Manual
K
19
Plastic structure optimization
Experience
Material selection
Material science
Unknown
Data
K
20
TRB technology
Experience
Processing technology
Engineering mechanics
Unknown
Manual
K
21
Flexible splicing process
Definition
Processing technology
Mechanical engineering
Unknown
Manual


### 6.2. Fuzzy Importance Calculation and First Knowledge Push

There are 3 experts in the decision-making team. For knowledgeK1,K2,…,K21, the indirect dominance between each element is compared according to its influence on engineering intent “lightweight,” then the evaluation matrix can be constructed as follows: (27)A=1,1,11,1,11,1,113,12,1113,12,1112,11,11⋯14,13,1215,14,1314,13,1212,11,1112,11,1113,12,11⋯1,2,31,2,31,1,22,3,41,3,41,2,31,2,31,2,31,1,21,1,11,1,11,1,1⋯15,14,1314,13,1214,13,1213,12,1114,13,1213,12,11⋯14,13,1214,13,1213,12,1113,13,1213,12,1113,12,11⋮⋮⋱⋮⋮⋱⋮⋮2,3,43,4,52,3,43,4,52,3,42,3,4⋯1,1,11,1,11,1,11,1,21,1,21,2,3⋯13,12,1114,13,1213,12,1112,12,1112,11,1113,12,111,1,21,1,21,2,31,2,32,3,41,2,3⋯12,11,1112,11,1113,12,111,1,11,1,11,1,1⋯12,12,1113,12,1113,12,113,4,51,2,32,3,4⋮⋮⋱⋮⋮⋱⋮⋮13,12,1113,12,1112,11,112,3,42,3,41,2,3⋯1,2,32,3,41,2,31,2,21,2,31,2,3⋯1,1,11,1,11,1,12,3,41,1,21,2,314,13,1214,13,1113,12,1113,13,1213,12,1113,12,11⋯1,2,21,1,21,2,314,13,1213,12,1114,13,12⋯14,13,1212,11,1113,12,111,1,11,1,11,1,1.According to (8), (9), (10), and (12), the evaluation matrix can be transformed as (28)A=1,1,10.39,0.67,1.00⋯0.23,0.31,0.440.44,0.83,1.00⋯1.0,1.67,2.671.33,2.67,3.671.0,1.67,2.671,1,1⋯0.23,0.31,0.440.31,0.44,0.83⋯0.28,0.39,0.670.33,0.44,0.83⋮⋮⋱⋮⋮⋱⋮⋮2.33,3.33,4.332.33,3.33,4.33⋯1,1,11.00,1.33,2.33⋯0.31,0.44,0.830.44,0.67,1.001.00,1.33,2.331.33,2.33,3.33⋯0.44,0.83,1.001,1,1⋯0.39,0.67,1.002.00,3.00,4.00⋮⋮⋱⋮⋮⋱⋮⋮0.39,0.67,1.001.67,2.67,3.67⋯0.33,2.33,3.331.00,2.00,2.67⋯1,1,11.33,2.00,3.000.28,0.39,0.830.33,0.44,0.83⋯1.00,1.67,2.330.28,0.39,0.67⋯0.36,0.61,0.831,1,1.According to (13)–(16), the initial weight of each knowledge node can be got as(29)R=0.238,0.302,0.314,0.210,0.372,0.392,0.380,0.400,0.174,0.448,0.546,0.114,0.194,0.190,0.222,0.198,0.203,0.135,0.156,0.323,0.308.According to the background data of platform, the frequencies of knowledge nodes retrieved within 48 hours are extracted, and the statistical results are shown in Table2. We introduce the retrieval factor γ=0.5 to transform the separation measures into crisp value by using formulas (17) and (18). Synthetical importance of design knowledge KEi can be calculated (Table 3). Therefore, we can rank all knowledge nodes according to their values of KEi.Table 2
Statistical results of knowledge nodes retrieved.
K
1
K
2
K
3
K
4
K
5
K
6
K
7
K
8
K
9
K
10
K
11
K
12
K
13
K
14
K
15
K
16
K
17
K
18
K
19
K
20
K
21
Total
n
8
11
10
7
17
22
16
13
6
8
20
1
9
8
14
12
7
8
3
9
6
215Table 3
Synthetical importance of design knowledge.
Value
KE1
0.138
KE2
0.177
KE3
0.180
KE4
0.121
KE5
0.226
KE6
0.247
KE7
0.227
KE8
0.230
KE9
0.101
KE10
0.243
KE11
0.320
KE12
0.059
KE13
0.118
KE14
0.114
KE15
0.144
KE16
0.127
KE17
0.118
KE18
0.086
KE19
0.085
KE20
0.182
KE21
0.168By setting thresholdKE0=0.24, we can identify the most important knowledge nodes KE6, KE11 in the knowledge domain corresponding to engineering intent “lightweight.” These knowledge nodes can be pushed into the corresponding subtask module to implement the first directional knowledge push.


### 6.3. Knowledge Network Model

According to the six-tuple modeling constructed above, the attribute matrixes of knowledge sets can be established:(30)E=e1,e2,…,enT,where row vector ei(i=1,2,…,n) stands for the attribute matrix of knowledge node Ki. The rules for attribute values are as follows.K
E
i
d (identification of knowledge) is used as a function of identification, so the unified value is 1. There are five types of knowledge (KEt): definition, design requirement, design principles, experience, and source material, corresponding to numerical values 1,2,3,4,5. The attribute value of application phase KEa can be determined by the order of design phase, which means that the attribute value of design thinking phase is 1, the attribute value of design strategy phase is 2, the attribute value of creative expression phase is 3, the attribute value of material selection phase is 4, the attribute value of processing technology phase is 5, the attribute value of ergonomic design phase is 6, and the attribute value of structural design phase is 7. The attribute value of knowledge field KEf is similar to the attribute value of application phase, but because of the distance between different knowledge fields, the attribute values should correspond to them. For example, “mechanical engineering” and “engineering and technical science basic disciplines” are similar, so the attribute values are 1 and 2, respectively. “Management” is far from “material science,” so the attribute values are 15 and 8, respectively. The attribute value of importance KEi corresponds to its value. In order to prevent the difference between the values of different attributes, the attribute value of importance will be expanded by ten times. The attribute value of knowledge expression KEe refers to the attribute value of knowledge type.Therefore, the attribute matrix of 21 knowledge nodes can be established as follows:(31)E=12211.38115481.77415521.80314651.213117122.26412112.47415722.27215482.30113651.01314732.43213583.20414410.59415481.18413731.141136121.14415721.272122151.184154150.86414480.85114531.82411511.684,where row vector of E stands for the attribute matrix of single knowledge node. As to knowledge node K1 and knowledge K2, the calculation process of attribute similarity between two knowledge nodes is as follows:(32)as121=x11-x1¯-x21-x1¯=1-1-1-1=0,as122=x12-x2¯-x22-x2¯=2-3.57-5-3.57=0.14,as123=x13-x3¯-x23-x3¯=2-4.76-4-4.76=2,as124=x14-x4¯-x24-x4¯=1-5.81-8-5.81=2.62,as125=x15-x5¯-x25-x5¯=1.38-1.54-1.77-1.54=0.07,as126=x16-x6¯-x26-x6¯=1-2.95-4-2.95=0.9,ans1,2=∑n=16as12n6=0.955.In order to facilitate the calculation, we use MATLAB to program above calculation process of attribute similarity. Similarly, the attribute similarity of knowledge nodes can be compared in pairs. The results are shown in Table4.Table 4
Attribute similarity of knowledge nodes compared in pairs.
N
1
N
2
N
3
N
4
N
5
N
6
N
7
N
8
N
9
N
10
N
11
N
12
N
13
N
14
N
15
N
16
N
17
N
18
N
19
N
20
N
21
N
1
0.000
N
2
0.955
0.000
N
3
0.943
0.528
0.000
N
4
1.455
0.660
0.845
0.000
N
5
0.727
1.185
1.163
1.652
0.000
N
6
0.445
1.077
1.055
1.543
0.685
0.000
N
7
0.538
0.617
0.562
1.050
0.605
0.493
0.000
N
8
0.893
0.238
0.757
0.865
1.260
1.138
0.688
0.000
N
9
1.465
0.670
0.855
0.057
1.595
1.487
0.993
0.808
0.000
N
10
0.898
0.643
0.922
0.743
0.965
0.800
0.360
0.705
0.733
0.000
N
11
1.423
0.468
0.813
0.808
1.490
1.312
0.918
0.530
0.752
0.605
0.000
N
12
0.805
0.723
0.702
1.017
0.872
0.693
0.633
0.785
1.007
0.607
0.665
0.000
N
13
0.977
0.022
0.540
0.648
1.163
1.055
0.595
0.217
0.648
0.622
0.447
0.702
0.000
N
14
0.627
0.672
0.983
0.852
1.100
0.992
0.532
0.553
0.838
0.272
0.797
0.845
0.650
0.000
N
15
0.840
0.918
0.897
1.098
0.553
0.905
0.778
1.100
1.085
0.852
1.043
0.425
0.897
0.880
0.000
N
16
0.462
0.540
0.485
0.993
0.678
0.570
0.077
0.765
1.003
0.437
0.995
0.710
0.548
0.493
0.745
0.000
N
17
0.913
1.545
1.523
2.012
0.813
0.992
1.085
1.740
2.012
1.445
1.970
1.352
1.523
1.473
0.927
1.038
0.000
N
18
0.323
1.242
1.220
1.869
0.943
1.295
1.168
1.330
1.812
1.528
1.560
0.942
1.220
1.650
0.770
1.228
0.410
0.000
N
19
1.048
0.393
0.912
0.687
1.425
1.317
0.857
0.178
0.677
0.550
0.422
0.630
0.372
0.422
0.968
0.920
1.895
1.485
0.000
N
20
1.113
0.365
0.503
0.675
1.327
1.218
0.758
0.587
0.732
0.452
0.357
0.532
0.370
0.527
0.773
0.685
1.687
1.383
0.408
0.000
N
21
0.740
0.728
0.543
1.388
0.660
0.855
0.805
0.967
1.398
1.165
1.023
0.578
0.750
1.193
0.773
0.728
0.353
1.097
1.122
0.713
0.000By setting thresholdans0=0.5, if ansi,j≤ans0=0.5, there is a relationship between the two knowledge nodes. Then an edge can be created between them. Therefore, the knowledge network model (Figure 6) can be constructed.Figure 6
The knowledge network model of 21 knowledge nodes.


### 6.4. Immunization Strategy in Complex Network

Using the immunization strategy proposed in Section5.1, the immune process of 21 knowledge nodes is as follows.Step 1.
Randomly select nodeK10 from all knowledge nodes inKN-model, setting threshold Kcut=3.Step 2.
According to the information shown in Figure6, the degree of K10 can be defined as 4.Step 3.
Because the degree ofK10 exceeds the threshold Kcut=3, immunize knowledge node K10, then randomly immunize a neighboring node of K10:K7.After the completion of immunization strategy, the knowledge network model is reconstructed, which is shown in Figure7.Figure 7
The reconstructed knowledge network model.


### 6.5. Network Topology Analysis and Second Knowledge Push

By using the method proposed in Section5.2, topology analysis of the reconstructed knowledge network model (Figure 7) can be carried out. The calculation results of the degree of each knowledge node and clustering coefficients in the network topology are shown in Table 5. Thus, the topology of the reconstructed knowledge network model is plotted, as shown in Figure 8.Table 5
Result of topology analysis.
K
1
K
2
K
3
K
4
K
5
K
6
K
7
K
8
K
9
K
10
K
11
K
12
K
13
K
14
K
15
K
16
K
17
K
18
K
19
K
20
K
21
K
3
5
1
0
0
1
/
1
2
/
3
1
5
2
1
3
2
2
6
3
1
CC
0
0.5
0
0
0
0
/
0
1
/
1
0
0.5
0
0
0
0
0
0.4
1
0Figure 8
The topology of the reconstructed knowledge network model.The statistical description of the knowledge network model is as follows:(1)
Separate out a Hub node:K19.(2)
Separate out a closely related knowledge group:CAK19,K2,K13,K11,K9,K20.(3)
Separate out two knowledge groups that are not closely related:CBK14,K16,K3,K1,K6,K18,K17,K21 and CCK15,K12.Among them, the knowledge group associated with the Hub nodeK19 is CAK19,K2,K13,K11,K9,K20. Therefore, Hub node K19 and knowledge group CAK19,K2,K13,K11,K9,K20 can be pushed into the corresponding subtask module to implement the second directional knowledge push.


### 6.6. Program Implementation

Double push strategy of knowledge for product design proposed in this paper can be implemented with the help of cloud platform “http://www.sunhooyun.com/”. Based on ASP.NET technology system and Browser/Server mode and supported by the MySQL database and JAVA language, this paper uses the research results to build a design knowledge aided innovation service platform for antalgic pump (Figure 9).Figure 9
Design knowledge aided innovation service platform for antalgic pump.
(a)
Program interface for creative knowledge push
(b)
Program interface for engineering knowledge pushThrough the program interface for creative knowledge push shown in Figure9(a), designers could receive double creative knowledge push to stimulate creative inspiration. Subsequently, designers completed conceptual design of antalgic pump. The conceptual sketches have been plotted as shown in Figure 10, and the final conceptual design is shown in Figure 11.Figure 10
Conceptual sketches of antalgic pump.Figure 11
Final conceptual design of antalgic pump.Then, through the program interface for engineering knowledge push shown in Figure9(b), designers can receive double engineering knowledge push to assist engineering design. The result of final design of antalgic pump is shown in Figure 12.Figure 12
Final design of antalgic pump.


## 6.1. Design Knowledge Modeling

Suppose there are 21 independent design knowledge items in the knowledge domain corresponding to engineering intent “lightweight.” Using the method proposed in Section3.2, 21 independent design knowledge items can be expressed in six-tuple modeling as follows:(26)KE=KEid,KEt,KEa,KEf,KEi,KEe.The meaning of each symbol is as follows:KE: knowledge in the engineering knowledge domain; KEid: identification of knowledge; KEt: the type of knowledge; KEa: application phase of knowledge; KEf: the field of knowledge, including material, processing technology, and structure; KEi: significance degree of knowledge, that is, the importance of knowledge in its domain; KEe: the expression of knowledge, including data, drawing, manual, and model. The structured description of every design knowledge is shown in Table 1.Table 1
Structured description of 21 design knowledge items.
K
E
i
d
K
E
t
K
E
a
K
E
f
K
E
i
K
E
e
K
1
Quality reduction rules
Design requirement
Design strategy
Mechanical engineering
Unknown
Data
K
2
Selection of light material
Source material
Material selection
Material science
Unknown
Manual
K
3
FEA model
Source material
Processing technology
Engineering and technology
Unknown
Model
K
4
CAD engineering analysis
Experience
Ergonomic design
Ergonomics
Unknown
Model
K
5
Parameter inversion
Definition
Structural design
Computer science
Unknown
Manual
K
6
Global optimization
Design requirement
Design thinking
Mechanical engineering
Unknown
Manual
K
7
Laminated structure (plate)
Source material
Structural design
Engineering and technology
Unknown
Drawing
K
8
Comparison of rigid plastics
Source material
Material selection
Material science
Unknown
Data
K
9
FEA model
Design principle
Structural design
Ergonomics
Unknown
Model
K
10
CAD strength calculation
Experience
Structural design
Engineering mechanics
Unknown
Drawing
K
11
Quality reduction rules
Design principle
Processing technology
Material science
Unknown
Manual
K
12
Machining condition restriction
Experience
Material selection
Mechanical engineering
Unknown
Manual
K
13
Selection of composite material
Source material
Material selection
Material science
Unknown
Manual
K
14
Hollow structure rule
Design principle
Structural design
Engineering mechanics
Unknown
Data
K
15
Computer simulation analysis
Design principle
Ergonomic design
Computer science
Unknown
Manual
K
16
Laminated structure (shell)
Source material
Structural design
Engineering and technology
Unknown
Drawing
K
17
Production management standards
Design requirement
Design strategy
Management
Unknown
Manual
K
18
Plastic cost manual
Source material
Material selection
Management
Unknown
Manual
K
19
Plastic structure optimization
Experience
Material selection
Material science
Unknown
Data
K
20
TRB technology
Experience
Processing technology
Engineering mechanics
Unknown
Manual
K
21
Flexible splicing process
Definition
Processing technology
Mechanical engineering
Unknown
Manual


## 6.2. Fuzzy Importance Calculation and First Knowledge Push

There are 3 experts in the decision-making team. For knowledgeK1,K2,…,K21, the indirect dominance between each element is compared according to its influence on engineering intent “lightweight,” then the evaluation matrix can be constructed as follows: (27)A=1,1,11,1,11,1,113,12,1113,12,1112,11,11⋯14,13,1215,14,1314,13,1212,11,1112,11,1113,12,11⋯1,2,31,2,31,1,22,3,41,3,41,2,31,2,31,2,31,1,21,1,11,1,11,1,1⋯15,14,1314,13,1214,13,1213,12,1114,13,1213,12,11⋯14,13,1214,13,1213,12,1113,13,1213,12,1113,12,11⋮⋮⋱⋮⋮⋱⋮⋮2,3,43,4,52,3,43,4,52,3,42,3,4⋯1,1,11,1,11,1,11,1,21,1,21,2,3⋯13,12,1114,13,1213,12,1112,12,1112,11,1113,12,111,1,21,1,21,2,31,2,32,3,41,2,3⋯12,11,1112,11,1113,12,111,1,11,1,11,1,1⋯12,12,1113,12,1113,12,113,4,51,2,32,3,4⋮⋮⋱⋮⋮⋱⋮⋮13,12,1113,12,1112,11,112,3,42,3,41,2,3⋯1,2,32,3,41,2,31,2,21,2,31,2,3⋯1,1,11,1,11,1,12,3,41,1,21,2,314,13,1214,13,1113,12,1113,13,1213,12,1113,12,11⋯1,2,21,1,21,2,314,13,1213,12,1114,13,12⋯14,13,1212,11,1113,12,111,1,11,1,11,1,1.According to (8), (9), (10), and (12), the evaluation matrix can be transformed as (28)A=1,1,10.39,0.67,1.00⋯0.23,0.31,0.440.44,0.83,1.00⋯1.0,1.67,2.671.33,2.67,3.671.0,1.67,2.671,1,1⋯0.23,0.31,0.440.31,0.44,0.83⋯0.28,0.39,0.670.33,0.44,0.83⋮⋮⋱⋮⋮⋱⋮⋮2.33,3.33,4.332.33,3.33,4.33⋯1,1,11.00,1.33,2.33⋯0.31,0.44,0.830.44,0.67,1.001.00,1.33,2.331.33,2.33,3.33⋯0.44,0.83,1.001,1,1⋯0.39,0.67,1.002.00,3.00,4.00⋮⋮⋱⋮⋮⋱⋮⋮0.39,0.67,1.001.67,2.67,3.67⋯0.33,2.33,3.331.00,2.00,2.67⋯1,1,11.33,2.00,3.000.28,0.39,0.830.33,0.44,0.83⋯1.00,1.67,2.330.28,0.39,0.67⋯0.36,0.61,0.831,1,1.According to (13)–(16), the initial weight of each knowledge node can be got as(29)R=0.238,0.302,0.314,0.210,0.372,0.392,0.380,0.400,0.174,0.448,0.546,0.114,0.194,0.190,0.222,0.198,0.203,0.135,0.156,0.323,0.308.According to the background data of platform, the frequencies of knowledge nodes retrieved within 48 hours are extracted, and the statistical results are shown in Table2. We introduce the retrieval factor γ=0.5 to transform the separation measures into crisp value by using formulas (17) and (18). Synthetical importance of design knowledge KEi can be calculated (Table 3). Therefore, we can rank all knowledge nodes according to their values of KEi.Table 2
Statistical results of knowledge nodes retrieved.
K
1
K
2
K
3
K
4
K
5
K
6
K
7
K
8
K
9
K
10
K
11
K
12
K
13
K
14
K
15
K
16
K
17
K
18
K
19
K
20
K
21
Total
n
8
11
10
7
17
22
16
13
6
8
20
1
9
8
14
12
7
8
3
9
6
215Table 3
Synthetical importance of design knowledge.
Value
KE1
0.138
KE2
0.177
KE3
0.180
KE4
0.121
KE5
0.226
KE6
0.247
KE7
0.227
KE8
0.230
KE9
0.101
KE10
0.243
KE11
0.320
KE12
0.059
KE13
0.118
KE14
0.114
KE15
0.144
KE16
0.127
KE17
0.118
KE18
0.086
KE19
0.085
KE20
0.182
KE21
0.168By setting thresholdKE0=0.24, we can identify the most important knowledge nodes KE6, KE11 in the knowledge domain corresponding to engineering intent “lightweight.” These knowledge nodes can be pushed into the corresponding subtask module to implement the first directional knowledge push.


## 6.3. Knowledge Network Model

According to the six-tuple modeling constructed above, the attribute matrixes of knowledge sets can be established:(30)E=e1,e2,…,enT,where row vector ei(i=1,2,…,n) stands for the attribute matrix of knowledge node Ki. The rules for attribute values are as follows.K
E
i
d (identification of knowledge) is used as a function of identification, so the unified value is 1. There are five types of knowledge (KEt): definition, design requirement, design principles, experience, and source material, corresponding to numerical values 1,2,3,4,5. The attribute value of application phase KEa can be determined by the order of design phase, which means that the attribute value of design thinking phase is 1, the attribute value of design strategy phase is 2, the attribute value of creative expression phase is 3, the attribute value of material selection phase is 4, the attribute value of processing technology phase is 5, the attribute value of ergonomic design phase is 6, and the attribute value of structural design phase is 7. The attribute value of knowledge field KEf is similar to the attribute value of application phase, but because of the distance between different knowledge fields, the attribute values should correspond to them. For example, “mechanical engineering” and “engineering and technical science basic disciplines” are similar, so the attribute values are 1 and 2, respectively. “Management” is far from “material science,” so the attribute values are 15 and 8, respectively. The attribute value of importance KEi corresponds to its value. In order to prevent the difference between the values of different attributes, the attribute value of importance will be expanded by ten times. The attribute value of knowledge expression KEe refers to the attribute value of knowledge type.Therefore, the attribute matrix of 21 knowledge nodes can be established as follows:(31)E=12211.38115481.77415521.80314651.213117122.26412112.47415722.27215482.30113651.01314732.43213583.20414410.59415481.18413731.141136121.14415721.272122151.184154150.86414480.85114531.82411511.684,where row vector of E stands for the attribute matrix of single knowledge node. As to knowledge node K1 and knowledge K2, the calculation process of attribute similarity between two knowledge nodes is as follows:(32)as121=x11-x1¯-x21-x1¯=1-1-1-1=0,as122=x12-x2¯-x22-x2¯=2-3.57-5-3.57=0.14,as123=x13-x3¯-x23-x3¯=2-4.76-4-4.76=2,as124=x14-x4¯-x24-x4¯=1-5.81-8-5.81=2.62,as125=x15-x5¯-x25-x5¯=1.38-1.54-1.77-1.54=0.07,as126=x16-x6¯-x26-x6¯=1-2.95-4-2.95=0.9,ans1,2=∑n=16as12n6=0.955.In order to facilitate the calculation, we use MATLAB to program above calculation process of attribute similarity. Similarly, the attribute similarity of knowledge nodes can be compared in pairs. The results are shown in Table4.Table 4
Attribute similarity of knowledge nodes compared in pairs.
N
1
N
2
N
3
N
4
N
5
N
6
N
7
N
8
N
9
N
10
N
11
N
12
N
13
N
14
N
15
N
16
N
17
N
18
N
19
N
20
N
21
N
1
0.000
N
2
0.955
0.000
N
3
0.943
0.528
0.000
N
4
1.455
0.660
0.845
0.000
N
5
0.727
1.185
1.163
1.652
0.000
N
6
0.445
1.077
1.055
1.543
0.685
0.000
N
7
0.538
0.617
0.562
1.050
0.605
0.493
0.000
N
8
0.893
0.238
0.757
0.865
1.260
1.138
0.688
0.000
N
9
1.465
0.670
0.855
0.057
1.595
1.487
0.993
0.808
0.000
N
10
0.898
0.643
0.922
0.743
0.965
0.800
0.360
0.705
0.733
0.000
N
11
1.423
0.468
0.813
0.808
1.490
1.312
0.918
0.530
0.752
0.605
0.000
N
12
0.805
0.723
0.702
1.017
0.872
0.693
0.633
0.785
1.007
0.607
0.665
0.000
N
13
0.977
0.022
0.540
0.648
1.163
1.055
0.595
0.217
0.648
0.622
0.447
0.702
0.000
N
14
0.627
0.672
0.983
0.852
1.100
0.992
0.532
0.553
0.838
0.272
0.797
0.845
0.650
0.000
N
15
0.840
0.918
0.897
1.098
0.553
0.905
0.778
1.100
1.085
0.852
1.043
0.425
0.897
0.880
0.000
N
16
0.462
0.540
0.485
0.993
0.678
0.570
0.077
0.765
1.003
0.437
0.995
0.710
0.548
0.493
0.745
0.000
N
17
0.913
1.545
1.523
2.012
0.813
0.992
1.085
1.740
2.012
1.445
1.970
1.352
1.523
1.473
0.927
1.038
0.000
N
18
0.323
1.242
1.220
1.869
0.943
1.295
1.168
1.330
1.812
1.528
1.560
0.942
1.220
1.650
0.770
1.228
0.410
0.000
N
19
1.048
0.393
0.912
0.687
1.425
1.317
0.857
0.178
0.677
0.550
0.422
0.630
0.372
0.422
0.968
0.920
1.895
1.485
0.000
N
20
1.113
0.365
0.503
0.675
1.327
1.218
0.758
0.587
0.732
0.452
0.357
0.532
0.370
0.527
0.773
0.685
1.687
1.383
0.408
0.000
N
21
0.740
0.728
0.543
1.388
0.660
0.855
0.805
0.967
1.398
1.165
1.023
0.578
0.750
1.193
0.773
0.728
0.353
1.097
1.122
0.713
0.000By setting thresholdans0=0.5, if ansi,j≤ans0=0.5, there is a relationship between the two knowledge nodes. Then an edge can be created between them. Therefore, the knowledge network model (Figure 6) can be constructed.Figure 6
The knowledge network model of 21 knowledge nodes.


## 6.4. Immunization Strategy in Complex Network

Using the immunization strategy proposed in Section5.1, the immune process of 21 knowledge nodes is as follows.Step 1.
Randomly select nodeK10 from all knowledge nodes inKN-model, setting threshold Kcut=3.Step 2.
According to the information shown in Figure6, the degree of K10 can be defined as 4.Step 3.
Because the degree ofK10 exceeds the threshold Kcut=3, immunize knowledge node K10, then randomly immunize a neighboring node of K10:K7.After the completion of immunization strategy, the knowledge network model is reconstructed, which is shown in Figure7.Figure 7
The reconstructed knowledge network model.


## 6.5. Network Topology Analysis and Second Knowledge Push

By using the method proposed in Section5.2, topology analysis of the reconstructed knowledge network model (Figure 7) can be carried out. The calculation results of the degree of each knowledge node and clustering coefficients in the network topology are shown in Table 5. Thus, the topology of the reconstructed knowledge network model is plotted, as shown in Figure 8.Table 5
Result of topology analysis.
K
1
K
2
K
3
K
4
K
5
K
6
K
7
K
8
K
9
K
10
K
11
K
12
K
13
K
14
K
15
K
16
K
17
K
18
K
19
K
20
K
21
K
3
5
1
0
0
1
/
1
2
/
3
1
5
2
1
3
2
2
6
3
1
CC
0
0.5
0
0
0
0
/
0
1
/
1
0
0.5
0
0
0
0
0
0.4
1
0Figure 8
The topology of the reconstructed knowledge network model.The statistical description of the knowledge network model is as follows:(1)
Separate out a Hub node:K19.(2)
Separate out a closely related knowledge group:CAK19,K2,K13,K11,K9,K20.(3)
Separate out two knowledge groups that are not closely related:CBK14,K16,K3,K1,K6,K18,K17,K21 and CCK15,K12.Among them, the knowledge group associated with the Hub nodeK19 is CAK19,K2,K13,K11,K9,K20. Therefore, Hub node K19 and knowledge group CAK19,K2,K13,K11,K9,K20 can be pushed into the corresponding subtask module to implement the second directional knowledge push.


## 6.6. Program Implementation

Double push strategy of knowledge for product design proposed in this paper can be implemented with the help of cloud platform “http://www.sunhooyun.com/”. Based on ASP.NET technology system and Browser/Server mode and supported by the MySQL database and JAVA language, this paper uses the research results to build a design knowledge aided innovation service platform for antalgic pump (Figure 9).Figure 9
Design knowledge aided innovation service platform for antalgic pump.
(a)
Program interface for creative knowledge push
(b)
Program interface for engineering knowledge pushThrough the program interface for creative knowledge push shown in Figure9(a), designers could receive double creative knowledge push to stimulate creative inspiration. Subsequently, designers completed conceptual design of antalgic pump. The conceptual sketches have been plotted as shown in Figure 10, and the final conceptual design is shown in Figure 11.Figure 10
Conceptual sketches of antalgic pump.Figure 11
Final conceptual design of antalgic pump.Then, through the program interface for engineering knowledge push shown in Figure9(b), designers can receive double engineering knowledge push to assist engineering design. The result of final design of antalgic pump is shown in Figure 12.Figure 12
Final design of antalgic pump.


## 7. Conclusions

The paper proposed a double push strategy of product design knowledge to implement the effective auxiliary function for product design process based on the theory and method of complex network. From the global perspective, the paper proposed a comprehensive method that can apply in almost all the engineering domain to help designers and engineers obtaining the knowledge they need most. Compared to traditional ones, this method is more effective and more operable, and it can improve the quality of innovative design and shorten the product design cycle.



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*Source: 2078626-2017-11-19.xml*