钻铣镗类加工中心的概念设计与方案评估研究

提出一种基于多色集合理论的机械产品概念设计的推理算法．采用一个有效的多目标方案评估算法，综合应用了层次分析法、模糊规则和神经网络，对满足需求的方案集进行评估和优化选择，得出满足需求的最优方案．以钻铣镗类加工中心的方案设计为例，用文中方法进行概念设计，得到了能满足需求的方案集和最优方案。     

A systematic decision making approach for product conceptual design based on fuzzy morphological matrix

Conceptual design plays an important role in development of new products and redesign of existing products. Morphological matrix is a popular tool for conceptual design. Although the morphological-matrix based conceptual design approaches are effective for generation of conceptual schemes, quantitative evaluation to each of the function solution principle is seldom considered, thus leading to the difficulty to identify the optimal conceptual design by combining these function solution principles. In addition, the uncertainties due to the subjective evaluations from engineers and customers in early design stage are not considered in these morphological-matrix based conceptual design approaches. To solve these problems, a systematic decision making approach is developed in this research for product conceptual design based on fuzzy morphological matrix to quantitatively evaluate function solution principles using knowledge and preferences of engineers and customers with subjective uncertainties. In this research, the morphological matrix is quantified by associating the properties of function solution principles with the information of customer preferences and product failures. Customer preferences for different function solution principles are obtained from multiple customers using fuzzy pairwise comparison (FPC). The fuzzy customer preference degree of each solution principle is then calculated by fuzzy logarithmic least square method (FLLSM). In addition, the product failure data are used to improve product reliability through fuzzy failure mode effects analysis (FMEA). Unlike the traditional FMEA, the causality relationships among failure modes of solution principles are analyzed to use failure information more effectively through constructing a directed failure causality relationship diagram (DFCRD). A fuzzy multi-objective optimization model is also developed to solve the conceptual design problem. The effectiveness of this new approach is demonstrated using a real-world application for conceptual design of a horizontal directional drilling machine (HDDM).     

AN INTEGRATED FUZZY APPROACH FOR DETERMINING ENGINEERING CHARACTERISTICS IN CONCRETE INDUSTRY

This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry.     

Universal triple I fuzzy reasoning algorithm of function model based on quotient space

Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design,the theory of quotient space and universal triple I fuzzy reasoning method are introduced,and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed.Firstly,the product function granular model based on the quotient space theory is built,with its function granular representation and computing rules defined at the same time.Secondly,in order to quickly achieve function granular model from function requirement,the function modeling method based on universal triple I fuzzy reasoning is put forward.Within the fuzzy reasoning of universal triple I method,the small-distance-activating method is proposed as the kernel of fuzzy reasoning;how to change function requirements to fuzzy ones,fuzzy computing methods,and strategy of fuzzy reasoning are respectively investigated as well;the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved.Lastly,the validity of the function granular model and function modeling algorithm is validated.Through our method,the reasonable function granular model can be quickly achieved from function requirements,and the fuzzy character of conceptual design can be well handled,which greatly improves conceptual design.     

基于模糊三角数模糊神经网络的软件质量评价方法

用户对软件质量的评价与其体验密切相关,但由于软件产品的抽象性、复杂性以及用户需求的模糊性,目前软件质量评价方法都缺乏对该方面内容的关注,忽略了用户需求在软件质量评价中的作用。针对于此,考虑用户需求对软件质量的影响,将用户需求作为一种特殊的软件特性,构建了基于模糊三角数的模糊神经网络来处理软件开发过程中用户需求同软件特性之间的非线性关系,符合软件产品复杂性的特点,使软件质量评价结果更客观、全面。结果表明,基于模糊三角数模糊神经网络能够更好地反映用户需求同软件特性之间的非线性关系,是一种研究软件综合质量评价的有效方法。     

Product portfolio identification based on association rule mining

It has been well recognized that product portfolio planning has far-reaching impact on the company's business success in competition. In general, product portfolio planning involves two main stages, namely portfolio identification and portfolio evaluation and selection. The former aims to capture and understand customer needs effectively and accordingly to transform them into specifications of product offerings. The latter concerns how to determine an optimal configuration of these identified offerings with the objective of achieving best profit performance. Current research and industrial practice have mainly focused on the economic justification of a given product portfolio, whereas the portfolio identification issue has been received only limited attention. This article intends to develop explicit decision support to improve product portfolio identification by efficient knowledge discovery from past sales and product records. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with requirement analysis enacted among customers, marketing folks, and designers. An association rule mining system (ARMS) is proposed for effective product portfolio identification. Based on a scrutiny into the product definition process, the article studies the fundamental issues underlying product portfolio identification. The ARMS differentiates the customer needs from functional requirements involved in the respective customer and functional domains. Product portfolio identification entails the identification of functional requirement clusters in conjunction with the mappings from customer needs to these clusters. While clusters of functional requirements are identified based on fuzzy clustering analysis, the mapping mechanism between the customer and functional domains is incarnated in association rules. The ARMS architecture and implementation issues are discussed in detail. An application of the proposed methodology and system in a consumer electronics company to generate a vibration motor portfolio for mobile phones is also presented.     

Perceived feature utility-based product family design: a mobile phone case study

To assure profit maximization through mass customization and personalization, effectively eliciting consumer needs across different market segments is critical. Although functional performance specifications and adequacy of various design forms can be measured directly and objectively, many designers and engineers struggle with clearly evaluating product criteria requiring subjective consumer input; the fact that these inputs change over time further complicates the process. To appropriately evaluate product criteria, an effective design decision-making analysis is required. In this study, we propose a methodology to assure effective elicitation of needs and their inclusion in design decision making and illustrate it using a mobile phone product family design scenario. First, consumer perceived utility of design features is gathered using a questionnaire (500+ responses) and then modeled using multi- attribute utility theory to facilitate the evaluation of a product family while responding to needs across customer clusters shaped by demographics. The methodology goal is to determine the relative goodness of a product family in comparison to its competition. We also compare and evaluate the application of the proposed method to conjoint analysis.     

A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution

Conceptual design evaluation plays a crucial role in new product development (NPD) and determines the quality of downstream design activities. Currently, most existing methods focus on fuzzy quantitative the evaluation information of multi-objectives in conceptual schemes selection. However, the above process ignores the various customers' preferences for each scheme under the evaluation objective, causing inconsistent preference weights in the various schemes, which cannot guarantee the market value of the optimal scheme. Furthermore, the ambiguous attitude from experts in the early design stage is not well taken into account. To this end, a conceptual scheme decision model with considering diverse customer preference distribution based on interval-valued intuitionistic fuzzy set (IVIFS) is proposed. The model is divided into three parts. Firstly, the initial decision matrix of multi-experts concerning the qualitative and quantitative design attributes is constructed based on intuitionistic fuzzy sets, and then the IFS decision matrix with interval boundaries is formed by using rough set technology. Secondly, the mapping model of design attribute to customer preference is constructed, and then the demand preference strategy implied by design attribute is judged. Thirdly, based on the demand preference strategy, the preferences’ weights for each scheme are calculated. Next, integrating the evaluation data with the same preference in the scheme, the comprehensive satisfaction of the scheme is obtained through IVIFS weighted aggregation operator, and then the optimal scheme is decided. Eventually, a case study of mobile phone form feature schemes is further employed to verify the proposed decision model, and results are sensitivity analyzed and compared.     

Using integrated quality function deployment and theory of innovation problem solving approach for ergonomic product design

A multidisciplinary approach integrating method of identification of customer satisfaction needs (CSNs), the House of Quality (HoQ) chart of quality function deployment (QFD), theory of innovation problem solving (TRIZ) and fuzzy group decision-making theory for ergonomic product innovative design and evaluation in the early design stages was proposed. An integrated model and the approach procedures consists of four steps. In step 1, identification of CSNs is based on a data source triangulation approach, questionnaire survey, 5-point liner numeric rating scale, factor analysis, and Cronbach’s coefficient alpha statistic are utilized to guarantee that the CSNs are complete and reliable. In step 2, a correlation matrix is built to identify the critical ergonomic design areas and the key problems are established by analysis of the negative relationships obtained from interrelationship half-matrix at the roof of the HoQ. In step 3, to solve the problems, TRIZ main tools and contradiction analysis are utilized. Several innovative alternatives are generated by combining appropriate Inventive Principles of TRIZ, the critical ergonomic design areas and the ergonomic design principles. In step 4, a general and easy fuzzy group decision-making method for evaluating of the best design alternatives is presented. A case study of the integrated kitchen stove innovative design and evaluation is conducted to demonstrate the applicability of the proposed approach.     

Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.     

基于混合功能推理的机械系统概念设计的研究

给出了产品设计说明形式化的描述,应用P/T网对原型机构抽象结构进行表示,利用编码技术对功能要求、功能约束及结构约束进行编码,建立了原型机构知识库。在所提出的基于设计说明和问题解重定义的正、反向混合推理方法的基础上,采用功能相似性度量的控制策略,进行复杂机械系统的概念设计,完成整个概念空间的构建。最后用实例对推理过程的自动性及方法的有效性进行了验证。     

一种基于改进T-S模糊推理的模糊神经网络学习算法

针对模糊神经网络学习算法计算量过大,在预测模型设计中提出了基于改进T-S模糊推理的模糊神经网络学习算法。主要工作如下:首先,改进T-S模糊推理方法,定义基于偏移率的T-s模糊推理方法;然后,通过将此模糊推理方法与基于合成规则的模糊推理方法及距离型模糊推理方法相比较可以看出,所提方法有较少的计算量,且比较有效;最后,在此基础上改善了模糊神经网络学习算法,并将其应用于天气预测与安全态势预测。测试结果表明,该方法明显改善了学习效率,减少了预测模型设计中的学习次数与时间复杂度,并降低了学习误差。     

A comparative study of uncertainty methods for legal reasoning

This paper is based on the premise that legal reasoning involves an evaluation of facts, principles, and legal precedent that are inexact, and uncertainty-based methods represent a useful approach for modeling this type of reasoning. By applying three different uncertainty-based methods to the same legal reasoning problem, a comparative study can be constructed. The application involves modeling legal reasoning for the assessment of potential liability due to defective product design. The three methods used for this study include: a Bayesian belief network, a fuzzy logic system, and an artificial neural network. A common knowledge base is used to implement the three solutions and provide an unbiased framework for evaluation. The problem framework and the construction of the common knowledgebase are described. The theoretical background for Bayesian belief networks, fuzzy logic inference, and multilayer perceptron with backpropagation are discussed. The design, implementation, and results with each of these systems are provided. The fuzzy logic system outperformed the other systems by reproducing the opinion of a skilled attorney in 99 of 100 cases, but the fuzzy logic system required more effort to construct the rulebase. The neural network method also reproduced the expert's opinions very well, but required less effort to develop. ©1999 John Wiley & Sons, Inc.     

基于FCBR与AHP混合机制的产品概念设计方法

以FCBR和AHP混合机制为基础构建一个面向产品概念设计的系统架构,该架构将模糊集原理应用于CBR,解决了概念设计时用户需求描述的不确定与不规范性问题,得到符合设计要求的评价模块集,应用AHP方法对评价案例集进行功能指标和经济指标上的评价,从而获得最合适的候选设计方案,通过调用功能改写案例库中的改写规则来修改候选案例得到最终优化方案。应用实例表明,在概念设计中使用该混合机制可以为用户提供有效的设计决策。     

一种分层的模糊知识表示与推理控制结构

提出了一种分层的模糊知识表示方法以及基于该方法的推理及控制结构,在模糊集合的基础上以抽象度描述了模糊概念在概念空间中的层次,用概念胞来表示概念空间中的模糊知识,用协同元和阈值描述推理的条件,推理以条件激活的方式进行扩展,并讨论了三种基本的扩展推理形式。     

基于自适应模糊网络的在线辨识

研究了基于一阶Sugeno的自适应网络模糊推理系统（ANFIS）进行在线辨识的方法。给出了该自适应网络的结构,在此基础上给出了网络权值的修正算法,即综合最陡下降法和最小二乘法得到的一种混合学习算法。对一个非线性模型进行了数字仿真,得到的在线辨识的结果优于采用反传算法的普通神经网络辨识方法。由此证明,一阶Sugeno模糊推理模型和混合学习算法的采用,使得该辨识方法具备网络结构简单、收敛速度快的优势,便于工程实现。     

基于神经网络的电信客户流失预测主题建模及实现

客户流失管理是电信运营商通过对客户需求满意度调查进行有针对性挽留客户的一个重要方法，其中最关键的就是对客户流失行为做出预测。提出了一种基于神经网络的客户流失预测模型。根据行业专家经验值选取分析变量，通过神经网络计算分析变量的权值，建立客户流失预测模型并对客户流失趋势进行预测。该方法与决策树和贝叶斯网络等算法相比，通过使用两次神经网络，从原始数据上千个属性中提炼出与客户流失度相关性较大的属性，分析出的影响流失属性更利于下一步的客户挽留工作。     

A hybrid approach to concept selection through fuzzy analytic network process

Evaluating conceptual design alternatives in a new product development (NPD) environment has been one of the most critical issues for many companies which try to survive in the fast-growing world markets. Therefore, most companies have used various methods to successfully carry out this difficult and time-consuming evaluation process. Of these methods, analytic hierarchy process (AHP) has been widely used in multiple-criteria decision-making (MCDM) problems. But, in this study, we used analytical network process (ANP), a more general form of AHP, instead of AHP due to the fact that AHP cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. Furthermore, in some cases, due to the vagueness and uncertainty on the judgments of a decision-maker, the crisp pairwise comparison in the conventional ANP is insufficient and imprecise to capture the right judgments of the decision-maker. Therefore, a fuzzy logic is introduced in the pairwise comparison of ANP to make up for this deficiency in the conventional ANP, and is called as fuzzy ANP. In short, in this paper, a fuzzy ANP-based approach is proposed to evaluate a set of conceptual design alternatives developed in a NPD environment in order to reach to the best one satisfying both the needs and expectations of customers, and the engineering specifications of company. In addition, a numerical example is presented to illustrate the proposed approach.     

Evaluation of design requirements using fuzzy outranking methods

To increase customer satisfaction, quality function deployment is used to translate customer needs into technical design requirements (DRs). Determination of DRs for product development is very important because these requirements are the vital keys to successful products. The methods used to evaluate DRs in the literature can be categorized into multicriteria evaluation methods such as scoring methods, the analytic hierarchy method, analytic network process, and so forth. There are few papers using fuzzy multi‐attribute outranking methods to evaluate DRs. This article aims to compare the results of three different fuzzy outranking methods to evaluate the DRs in the PVC windows industry. A sensitivity analysis is also made by using the software, FOuR. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1229–1250, 2007.