质量功能展开中关联关系确定的RBF方法

针对质量功能展开中存在顾客需求和工程特性之间的关联关系不确定和模糊等特质,提出了质量功能展开中关联关系的径向基函数(Radial Basis Function,RBF)神经网络方法。采用三层RBF神经网络,由质量屋中顾客需求组成RBF神经网络的输入层神经元,中间层选用高斯核函数,工程特性组成RBF神经网络输出层神经元,由顾客需求和工程特性关联关系评价样本集组成网络的训练样本集,通过网络训练的方式来获得最优的顾客需求与工程特性的关联关系。最后,结合天然光采光产品开发,进行了实例分析,说明该方法具有计算速度快,拟合精度高的特点。     

基于扩展QFD模型的包装机械质量成本分析

在QFD质量屋基础上提出扩展QFD模型,引入质量保证成本评价指标、质量损失成本评价指标及技术要求综合评价指标,对顾客需求转化而成的技术需求进行成本分析,为包装机械产品开发及设计人员选择和确定实际的技术特征提供科学的依据.包装机设计的应用实例表明,利用扩展的QFD模型能够快速、有效的进行产品质量成本分析.     

基于顾客需求的业务过程分析

 应用QFD的思想,建立了顾客需求一绩效指标关系矩阵、绩效指标一过程关系矩阵,以此为基础获得了顾客需求过程关系矩阵从而建立了顾客需求与实现需求的过程的直接联系.提出了计算待改进过程优先度的方法,为识别出那些主要影响顾客需求的过程、确定过程管理的方向提供了可靠的依据.最后开展了案例研究.     

基于QFD与FBS模型的坐便器多适性设计研究

目的 设计能够适应多类用户群体的多适性坐便器,让老年用户也能够平等的使用。方法 以针对一般群体的主流坐便器为设计基础,合理融入老年人群的特殊需求。运用质量功能展开（Quality Function Deployment,QFD）建立多适性坐便器用户需求与设计要求关系矩阵,确定关键设计要求的目标作为功能引入FBS（Function-Behavior-Structure）模型,最终获得坐便器结构化信息。结论 结合运用QFD和FBS模型展开的坐便器多适性设计研究,能够精确有效地将用户需求转化为设计要求,使最终的产品客观表现与用户需求之间能够精准映射匹配。同时验证了QFD和FBS融合应用的可行性和有效性。     

基于QFD和DOE的反光镜膜层表面缺陷改进研究

本文以光学反光镜为研究对象,通过QFD(Quality Function Deployment)获取顾客对光学反光镜膜层质量需求以及产品生产过程中所产生的质量缺陷统计,结合光学反光镜镀膜工艺,将这些需求以质量屋的形式转换为薄膜镀制过程中影响膜层质量的众多工艺因素,运用DOE(Desiqn of Experiment)试验方法对关键工艺因素进行优化设计、试验过程实施,试验结果采用检验测试、极差分析等方法进行分析,最终获得针对膜层表面缺陷改进的最优工艺参数组合。经试验验证,所构建的顾客需求为导向的QFD和DOE集成改进模型在反光镜膜层表面性能缺陷改进方面均取得了较好的实际应用效果。     

基于QFD的汽车造型设计特性优先度评价方法

目的为了客观准确地获取设计前期汽车造型设计的重点方向,提出一种改进的QFD质量屋矩阵评价方法,进行汽车造型设计特性优先度分析。方法在该方法中,运用访谈、问卷调研、数理分析方法获取顾客满意需求及重要度,并采用Cronbachα系数进行信度检测,确保数据结果的可靠性,依据汽车形态特征群确立其造型设计特性。通过顾客需求与汽车造型设计特性关系矩阵计算出设计特性分值。结论确立了汽车造型设计特性优先度,从而证明了方法的可行性。     

基于QFD的外卖包装设计顾客需求分析模型构建

目的 为满足企业在疫情下对外卖产品包装质量提供更加准确的改进方向和快速决策争取宝贵时效,提出一种基于QFD研究框架的包装设计模型.方法 该模型在传统QFD的基础上运用粗糙集获取顾客需求初始权重,避免需求分析过程中的主观弊端,再结合Kano模型将顾客需求进行分类,最终确定顾客的需求重要度.结果 通过模型分析得出了前3个需要重点改进的顾客需求,分别为有防疫提示、包装严实、一次性开启,其最终重要度分别为0.22,0.21和0.18.结论 运用该模型可以使外卖包装设计更加准确地满足客户需求,同时也为外卖包装服务质量的改进提供了合理的工具方法.     

一种基于约束的并行设计方法研究

在简化QFD分析过程和矩阵框架的基础上，提出了一种支持并行工程的设计约束展开（DCD）方法。该方法将适用对象由QFD方法中单纯的顾客需求扩展到并行工程整个产品生命周期中的所有约束，使得分析过程更具灵活性，并为并行设计过程提供了一个一致的环境。讨论了DCD方法中约束的确定和矩阵的结构与算法两个问题，并以DFA为例给出了一个DCD方法的应用实例。     

基于QFD与FBS的可移动电力检测设备创新设计

目的为了客观准确地将目标用户认知需求转化为产品技术特性的设计要求,提升可移动电力检测设备目标用户认知需求满意度及明确产品创新方向,提出QFD和FBS集成的创新设计模式。方法首先,通过访谈和问卷调研获取用户需求,借助AHP法求解用户认知需求权重;其次,运用QFD法构建质量屋,明确可移动电力检测设备待改进的关键技术特性指标;最后,将用户旅程图引入FBS的功能—行为—结构展开模型中,以解决可移动电力检测设备的关键技术特性问题,指导产品创新设计。结论QFD与FBS理论的融合应用能够将用户认知需求精准转化为技术特性层面的设计要求,对可移动电力检测设备的人机、造型、功能、收纳等方面的不足提出了相应的解决方案,实现了用户认知需求与产品技术特性之间的映射转换,证明了该方法具有可行性。     

基于QFD-TRIZ的电热水器设计

目的 为明确电热水器产品功能要素与需求要素的对应关系,提高电热水器用户的使用满意度。方法 通过构建QFD与TRIZ的集成设计模型,使用亲和图筛选分析顾客需求,利用QFD整理顾客需求权重并构建质量屋,从而明确关键设计要素;通过TRIZ分析方法判断了质量屋中的矛盾类型后,将设计要求转换为与工程要素间的矛盾,并利用阿奇舒勒矩阵中相应的发明原理,提出了解决矛盾的方法。结果 通过模糊综合评价法筛选最优设计方案,最终的评估结果表明新的设计方案满足了实际市场用户需求的目标问题优化。结论 从用户被动发现产品零部件寿命耗尽转换为产品信息主动提示,并具备辅助用户操作的结构,提升了用户需求与产品功能的匹配度,可见质量功能展开理论与发明问题解决理论的集成设计方法对产品概念设计阶段的重要性,为之后的室内智能家电设计研究提供了参考。     

Optimal new product design using quality function deployment with empirical value functions

The house of quality (HOQ) has been widely proposed as a method for capturing the voice of the customer (VOC) when developing requirements for new products. The methodology has lacked a formal mechanism for trading off customer preferences with technical feasibility and economic reality. We propose a non‐linear mathematical program for determining the optimal engineering specifications during new product development as a function of elicited customer value functions, engineering development and production costs and development time constraints. The model is shown to be computationally feasible for realistic‐size problems and to outperform heuristic approaches. Copyright © 1999 John Wiley & Sons, Ltd.     

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.     

Quality Function Deployment Implementation in Construction: A Systematic Literature Review

The construction of new buildings represents a significant investment. The goal of new building construction is to maximize value and minimize cost while staying on time and within budget. Translating customer requirements into engineering terms for new construction design is vital for a construction project to be successful. Quality function deployment has been successfully used in product development to capture the voice of the customer and translate it into engineering characteristics. Quality function deployment then carries these parameters into production and service to ensure the voice of the customer is being met with the final product. The house of quality, a tool within quality function deployment, can provide a means for comparison of owner’s project requirements and the proposed design, along with identifying how the design decisions impact meeting customer requirements and green building requirements. Quality function deployment can effectively link the project phases through design and construction and into operations and maintenance to ensure the owner’s project requirements are met with the final building. This research identifies and categorizes studies of quality function deployment applications in construction. The research method used is a systematic literature review from databases related to quality function deployment in the construction industry published in the periodicals through 2016. The principal findings of implementations, practices, and integrated approaches are then summarized. This article intends to propel further research of quality function deployment in the construction sector.     

A methodology of integrating affective design with defining engineering specifications for product design

Affective design and the determination of engineering specifications are commonly conducted separately in early product design stage. Generally, designers and engineers are required to determine the settings of design attributes (for affective design) and engineering requirements (for engineering design), respectively, for new products. Some design attributes and some engineering requirements could be common. However, the settings of the design attributes and engineering requirements could be different because of the separation of the two processes. In previous studies, a methodology that considers the determination of the settings of the design attributes and engineering requirements simultaneously was not found. To bridge this gap, a methodology for considering affective design and the determination of engineering specifications of a new product simultaneously is proposed. The proposed methodology mainly involves generation of customer satisfaction models, formulation of a multi-objective optimisation model and its solving using a chaos-based NSGA-II. To illustrate and validate the proposed methodology, a case study of mobile phone design was conducted. A validation test was conducted and the test results showed that the customer satisfaction values obtained based on the proposed methodology were higher than those obtained based on the combined standalone quality function deployment and standalone affective design approach.     

Maximizing customer satisfaction by optimal specification of engineering characteristics

The House of Quality has been widely discussed as a mechanism for converting customer attributes into engineering characteristics to ensure the design quality of new products and processes. In the past, this process has been subjective and heuristic. In this paper, we present a mathematical programming model for determining the optimal settings for engineering characteristics based on value functions constructed to capture customer preferences. The model can be used with either traditional subjective measures of customer preference or incorporate empirical models based on quantitative data. The robustness of the optimal solution to randomness in parameter estimates is investigated. An example is used to demonstrate the procedure.     

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。     

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.