QFD中关联关系确定的正交试验设计方法

顾客需求与工程特性之间关联关系的确定是QFD规划的难点和重点.针对大量数据难以获取的问题,提出确定QFD关联关系的正交试验设计方法.将工程特性作为影响因素,顾客需求作为评价指标,从而看成多因素多指标的试验设计问题.利用方差分析的P值确定工程特性对顾客需求的影响程度,将各工程特性的方差贡献率作为顾客需求与工程特性的关联函数的系数.最后.结合全自动洗衣机开发.进行了实例分析,说明该方法可采用较少量的数据,获得比较满意的结果.     

基于RBF和BP神经网络的低真空管道高速列车气动阻力预测对比研究北大核心CSCD

为了实现对低真空管道中运行列车的最大阻力预测研究,本文采用数值仿真和神经网络结合的方法。选取不同阻塞比、运行速度和管道压力,利用流体仿真软件计算100种运行工况下列车的最大阻力;以96组仿真数据作为网络模型训练样本,选取RBF和BP两种三层神经网络,经多次调试确定最佳隐层神经元数目,利用训练函数训练两种预测模型;利用随机选取的4组验证样本验证两种网络模型。研究表明:RBF和BP神经网络模型能较好的预测列车在真空管道中运行的最大阻力,其中RBF神经网络预测值的最大误差不高于5%,相比BP神经网络,RBF预测精度更优。     

基于RBF和BP神经网络的低真空管道高速列车气动阻力预测对比研究

为了实现对低真空管道中运行列车的最大阻力预测研究,本文采用数值仿真和神经网络结合的方法。选取不同阻塞比、运行速度和管道压力,利用流体仿真软件计算100种运行工况下列车的最大阻力;以96组仿真数据作为网络模型训练样本,选取RBF和BP两种三层神经网络,经多次调试确定最佳隐层神经元数目,利用训练函数训练两种预测模型;利用随机选取的4组验证样本验证两种网络模型。研究表明:RBF和BP神经网络模型能较好的预测列车在真空管道中运行的最大阻力,其中RBF神经网络预测值的最大误差不高于5%,相比BP神经网络,RBF预测精度更优。     

双曲正切非线性函数和加均值量化算法对BP神经网络图像压缩处理的影响

为了有效改善整个网络神经元的信息处理的特性,采用了双曲正切S形非线性传输函数和加均值量化算法,建立了BP神经网络图像压缩处理系统。由实验分析发现采用双曲正切传输函数的神经网络对图像处理的质量有所改善,并证明了这种传输函数的有效性。在此基础上又采用了加均值量化算法来提高神经网络图像压缩处理的训练速度。经过实验证明这种方法不仅不影响神经网络图像处理的质量而且缩短了网络传输图像的时间,由此证明了所提出算法的可行性。     

基于RBF神经网络的Bootstrap数据扩充方法及其在IRSS可靠性估计中的应用

针对高可靠性、长寿命产品可靠性试验数据样本较少难以进行有效可靠性评估问题,提出一种基于RBF神经网络的Bootstrap数据扩充方法,利用RBF神经网络获取原样本连续分布特性,邻域函数构建网络输入集。仿真表明,由该扩充方法获得的扩充样本分布特性更接近于其真实分布,并有效利用了原样本取值区间上、下限数据信息,拓展更宽的样本取值范围。将其应用于工业机器人伺服系统(IRSS)伪失效寿命分布可靠性评估中,扩充伪失效寿命数据,获得IRSS有效可靠性评估结果,表明方法的实际应用价值。     

基于Rough集理论的模糊神经网络构造方法

提出了在模糊神经网络中使用Rough集理论进行网络结构设计的方法。由于Rough集理论有强大的数值分析能力,而模糊神经网络具有准确的逼近收敛能力和较高的精度,所以通过两者的结合,可以得到一种可理解性好、计算简单、收敛速度快的神经网络模型。这种网络构造方法的主要过程为：首先,利用Rough集理论对给定数据集进行规则获取;然后,根据这些规则构造模糊神经网络各层的神经元个数及相关参数初始值;最后,用BP算法迭代求出网络的各种参数,完成网络的设计。给出了一个二维非线性函数拟合的实例,进一步验证了方法的正确性。     

基于神经网络的电机转矩软测量研究

径向基函数神经网络（RBFNN）具有最优逼近和全局逼近的特性，在函数拟合方面优于传统的BP网络，将在化工领域广泛使用的软测量技术应用于电机系统的转矩测量，该方法的可行性进行了论证，并运用RBF神经网络建立转矩的软测量模型。同时建立了基于BP神经网络的软测量模型，用改进的kvenberg—Marquardt算法对BP神经网络进行学习和训练，并对两种网络进行了对比。该方法只需要电流信息，辨识方法简单。研究表明，RBF神经网络辨识效果优于BP神经网络。     

基于径向基函数神经网络的编织复合材料结构脱层损伤监测研究

鉴于传统的BP网络的速度慢和局部极小值问题，以及针对基于实验数据训练神经网络存在样本不足的缺陷，文中提出了利用径向基函数（Radial Base Function，简记为RBF）神经网络通过有限元方法对含有脱层损伤的复合材料试件进行数值模拟，把前五阶弯曲模态频率进行修正，以修正后的前五阶弯曲模态频率再经过归一化处理构建训练样本的新思路，将实验模态分析结果经归一化处理后送入训练好的RBF神经网络进行预测，从而实现对编制复合材料梁的脱层损伤定位和损伤程度评估。最后给出了编织复合材料结构损伤大小伤识别及定位的算例，仿真结果表明RBF神经网络速度快，稳定性好，精度高，在复合材料结构损伤监测中具有光明的应用前景和重要的工程应用价值。     

基于径向基函数神经网络的滚动轴承故障模式的识别

径向基函数（RBF）神经网络是一种3层前馈性神经网络，它具有较强的函数逼近能力和分类能力。鉴于径向基函数神经网络的优点，在对滚动轴承振动信号特征分析的基础上，提出了采用时序方法对其建立AR模型，利用AR模型参数建立径向基函数神经网络，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

关于颜色空间转换的RBF网络动态子空间自动划分辨识方法研究

以RGB与CIEL*a*b*颜色空间转换为例,采用径向基函数(RBF)神经网络,研究了颜色值在不同颜色空间之间的转换。利用基本采样数据集建立了颜色空间转换RBF网络模型,并通过增加样本数据,采用动态规划颜色子空间的方法,提高了模型转换精度。研究结果显示,该方法的转换速度和精度都优于基于动态子空间自动划分的BP神经网络颜色空间转换方法。     

A fuzzy expected value-based goal programing model for product planning using quality function deployment

Product planning is one of four important processes in new product development using quality function deployment (QFD), which is a widely used customer-driven approach. In this article, a hierarchical framework for product planning using QFD is developed. To tackle the fuzziness in functional relationships between customer requirements and engineering characteristics (ECs) in product planning, the least squares method is incorporated into fuzzy regression to investigate those functional relationships, by which a more central tendency can be obtained. Furthermore, a fuzzy expected value-based goal programing model is proposed to specify target values of ECs. Different from some fuzzy product planning approaches for QFD, the proposed programing model has unambiguous interpretations. An illustrated example of a quality improvement problem of emulsification dynamite-packing machine design is given to demonstrate the application and performance of the proposed approach.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

基于质量功能展开方法的电动自行车设计优先度研究

以质量功能展开方法在新产品开发中的应用为启示,从电动自行车设计角度,阐述了质量功能展开在确定该产品消费者需求与设计属性优先度的方法应用。首先,介绍了质量功能展开方法。进而通过电动自行车消费者需求、设计属性、质量功能展开矩阵分析,建立消费者需求与设计属性排序,从而该产品设计提供改善重点依据。     

质量屋中顾客需求向技术特征映射的一种方法

在利用质量功能配置(QualityFunctionDeployment,QFD)进行产品规划决策时,根据顾客需求选取技术特征,是实现产品规划中顾客域到技术特征域转换进而进行产品规划的关键。建立了资源约束下最大化顾客满意程度和最大化技术特征集表现度的整数规划模型,实现了顾客需求域到技术特征域的映射,从而确定出质量屋决策中需重点考虑的技术特征项,并给出了实例。     

Proceedings Reviews

Abstract:

Quality Function Deployment (QFD) is a systematic process for capturing and integrating the voice of the customer into every aspect of the design and delivery of products and services. Understanding customer wants or needs is crucial to the successful design and development of new products and services. QFD is a system that utilizes customer demand information to design products or services that will meet a client's mission. In addition, the process prioritizes and deploys these customer-driven characteristics throughout the product or service development to meet the customer needs, wants, and expectations. QFD determines effective development targets for the prioritized product and service characteristics. The QFD process has been used and documented extensively in product development. The service industry, however, lacks in the application of this process. The purpose of this article is to show practitioners and researchers how this process, in its entirety, can be used as a planning process to link customer requirements and service characteristics in the service industry. A case study was developed in which QFD was applied to develop recommendations for the American Society of Engineering Management (ASEM) in an effort to increase customer satisfaction and to identify opportunities to improve member benefits. The results of this study are applicable to any organization to improve the design and delivery of products and service regardless of industry.     

QFD在质检机构顾客满意度评价的运用

质量功能展开是一种顾客驱动的产品开发方法,目前广泛应用于多个领域的产品开发、质量管理的改进工作中。本研究尝试将质量功能展开方法引入质检机构顾客满意度评价工作,构建了"顾客满意度评价质量屋",建立了较完善的可数量化质检机构顾客满意度指数,形成顾客满意度指数综合评价体系。并以C所为案例进行了实测评价和改进探索。     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

Using Radial-basis Function Network for CLV

Analysis and comparing with three existing and popularly used forcasting customer lifetime value(CLV) methods,which are the Dwyer method,customer event-method and fitting method,and using performances of the existent artificial neural network,we apply the Radial-basis Function (RBF) network to forecast the CLV,the RBF network can approach the objective function partially.To every input/output pairs,it only needs adjust the weight a little and learn quickly which is very important to the forecast precision.Simulation and experimental results on the customers‘ data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.