基于6σ的鲁棒优化设计思想和方法

基于6σ的鲁棒设计优化方法把结构可靠性、鲁棒质量工程以及6σ质量工程原理等不同概率分析方法中的概念加以整合,可以从约束满足的概率和性能目标的灵敏度这两个与设计“σ水平”有关的方面来评价设计质量,使得全面的概率优化设计得以实施。介绍这种设计思想的基本概念、数学描述和实施过程。     

6σ质量管理法(三):认准6σ管理的方法学--了解与运用6σ设计工具DMADV

随着6σ法的普通采用迫切需要解决的问题是，指导6σ设计、过程实施的方法和辅助6σ法运用的丰富工具与基于微机的软件系统。本文重点介绍6σ的方法学，包括：6σ设计工具DMADV、6σ过程问题求解、预防与改进工具DMAIC及其支持软件——MINITAB系统。     

6σ公差堆叠分析法在产品结构设计中的应用和探索

现代电子产品更新迭代快,研发周期短,质量要求高,传统的公差堆叠分析方法已经不能充分适应现代制造企业的需求,研究6σ公差堆叠分析法具有重要的理论意义和实践价值。基于正态分布理论和概率统计原理,结合SPC技术,给出了面向质量目标的6σ公差堆叠分析法的数学原理模型。利用Excel函数,以Excel软件为载体,建立了企业级公差堆叠分析电子模板。利用公差堆叠分析电子模板,在产品设计阶段,可以精准算出未来产品的不良率,或根据不同的质量要求,动态地订定零件公差,进行并行设计。     

基于双响应面模型的6σ稳健设计

双响应面法是通过试验设计分别构造响应均值和方差的近似模型，并通过该模型进行优化的一种稳健设计方法。文中对实验设计方法、响应曲面模型建立以及优化策略等进行分析。为实现质量管理与产品设计的协调，将质量工程中的6σ理念与双响应面法相结合，提出基于双响应面模型的6σ稳健优化设计方法。与传统优化方法相比，该方法不仅提高了优化效率，而且提高了设计的可靠性和稳健性。将其应用于结构优化设计和金属板料的拉深成形优化设计，均获得良好的结果。     

数字化6σ设计平台的设计与应用

基于6σ设计的流程管理模式和并行设计技术路线,将质量工具和设计方法融合在6σ设计的流程之中,提出了6σ设计的集成框架。结合项目管理的思想,应用过程管理技术和企业信息建模技术进行平台的总体结构设计和关键问题研究,设计并实现了基于C/S模式的数字化6σ设计平台。实际应用的结果表明,6σ设计的数字化平台可实现对多项目的集成化和动态管理,优化产品的设计和制造过程,从而降低设计成本,提高产品质量,增强市场竞争能力。     

基于6σ的多学科设计优化

在实际的工程问题中,总是不可避免地遇到一些不确定因素,导致许多优化策略无法用数学模型来体现这些不确定因素,从而造成结果分析错误或不全面．因此引入面向6σ概率设计(DFSS,σ为随机变量的标准偏差)方法,对其算法原理进行了阐述和分析,并且采用多学科设计优化(MDO)高层优化算法框架来支持DFSS设计,从而可以有效避免DFSS在处理复杂问题时串行处理的缺点,将涉及多学科的复杂系统问题逐步分解为各个单学科来并行地进行概率设计．最后,在理论研究的基础上结合轴承座这个工程实例,给出了其具体实现,证明了将二者结合进行复杂问题的概率设计是可行的．     

6σ概率优化设计方法及其应用

将6σ概念与遗传算法和蒙特卡罗模拟技术相结合，构造了基于产品质量工程的6σ概率优化设计方法，实现了对设计目标的优化，提高了产品的可靠性与稳健性。以悬臂焊接结构和弹簧的优化设计为应用实例，对优化结果的可靠性与稳健性进行了评估。     

6σ质量管理法(一):理解6σ竞赛的本质--获取与控制高质量的科学基础

从本期起本刊开设讲座专栏，由清华大学工业工程系罗振璧教授讲授6σ质量管理法。共分四讲：(一)理解6σ竞赛的本质——获取与控制高质量的科学基础；(二)认清6σ管理法的本质——以顾客为中心的管理；(三)认准6σ管理的方法学——了解与运用6σ设计工具DMADV；(四)认准6σ管理的方法学——     

6σ质量管理法(二):认清6σ管理法的本质--以顾客为中心的管理

企业产品与服务的质量与质量问题的解决必然涉及管理，但是，如果认为6σ法就只是一种质量管理的方法必将导致实施6σ管理法的失败。6σ法是一种特定、带战略性、系统地管理企业组织的方法，涉及市场、顾客、产品、服务、设计、流程、质量、价值链与财务业绩等众多领域，是它们的匹配、融合与集成。6σ法肯定不是一种快速     

门式启闭机门架结构6σ稳健优化设计

以重量减轻为目标,对启闭机门架结构的确定性优化结果进行可靠性分析,优化结果的可靠度为86.5％,可靠度不高.针对传统确定性优化设计中没有考虑不确定因素对产品性能和质量的影响,其优化结果可靠性和稳健性往往较低的问题.基于6σ质量设计方法、蒙特卡洛模拟法、多岛遗传算法,采用多学科设计优化软件ISIGHT对门架结构进行6σ-稳健优化设计.计算结果表明,经6σ稳健优化后门架结构自重降低了21.6％,其可靠性和稳健性显著提高,可靠度达到98％,为门式启闭机门架优化设计提供理论指导.     

基于模糊评价的六西格玛项目选择研究

由于六西格玛项目选择是成功实施六西格玛项目管理的最重要环节之一,针对六西格玛项目选择的特点,结合模糊评价方法,建立了六西格玛项目选择的模糊评价流程,在分析六西格玛项目评价指标体系的基础上,提出了六西格玛项目选择的模糊评价方法。最后通过重庆某大型企业的实例应用,验证了该方法的合理有效性,提高了企业选择六西格玛项目的精确性,使企业实施最优化项目而获得最大利益和成功,也拓展了六西格玛项目管理应用研究的内容。     

六西格玛设计与企业产品创新管理

通过对六西格玛设计方法的深入研究,阐述了其在汽车行业内的新技术新产品开发上的运用.结合六西格玛方法和六西格玛设计方法的比较,进一步分析了DFSS在流程和应用阶段的优越性,凸显了该方法在企业创新管理中的必要性.     

Using Six Sigma

Lean and Six Sigma are two widely acknowledge business process improvement strategies available to organizations today. Lean production is a strategy for achieving continuous improvement in business process performance through the elimination of waste. Six Sigma, on the other hand, is a powerful business strategy used for driving out variability from processes through effective utilisation of statistical tools and techniques. Lean Sigma is an integration of two powerful strategies which can have a dramatic impact on an organisation's ability to do the things right first time, leading to higher quality, higher productivity, faster delivery and lower costs. Lean Sigma combines the variability reduction tools and techniques from Six Sigma with the waste and non-value added elimination tools and techniques from lean production, to generate monetary savings to the bottom-line of an organisation. In this paper, the results of a survey on current application of Lean Sigma principles for waste reduction, variability reduction and enhanced customer satisfaction are discussed. Lean Sigma is a powerful strategy that can take any manufacturing or service company to a world class performance level. Today many organisations are integrating these two strategies for reducing costs and generating hard-dollar savings to the organisations' bottomline. Both strategies are complimentary to each other.     

基于多重相关特征质量损失函数的公差优化设计

在重点推导了具有多重相关特征产品的质量损失与尺寸公差的函数关系的基础上,提出了多重相关特征产品的公差优化设计方法,建立了基于制造成本一质量损失的公差优化设计的综合模型,建立该模型的目的是寻求制造成本和质量损失之间的平衡,实现旨在提高产品质量和降低成本的公差优化设计。应用实例验证了所提出方法的有效性。     

压力容器的结构优化设计及其具体实现

基于有限元分析程序和iSIGHT软件平台，对一薄壁压力容器进行了结构优化设计，介绍了具体的程序集成方法和自动寻优过程，得到了优化的确定性设计方案。考虑设计变量的随机性质，考察了确定性设计方案的可靠性。为了提高设计方案的可靠性，进行了一系列的6σ稳健设计，探讨了设计方案的最优性与稳健性之间的辨证关系。     

Lean Sigma [production and supply chain management]

Lean strategy brings a set of proven tools and techniques to reduce times, inventories, set up times, equipment downtime, scrap, rework and other wastes of the hidden factory. The focus is on value from a customer perspective and flowing this through the entire supply chain. The statistically based problem solving methodology of Six Sigma delivers data to drive solutions, delivering dramatic bottom-line results. In the Six Sigma school of study, a problem is tackled by a black or green belt, depending on the nature of the problem and the degree of complexity involved in the determination of solutions. The authors compare and contrast the lean and Six Sigma approaches, and find that greater benefits can be reaped by blending the best of each.     

六西格玛方法优化设计微混控制器

用六西格玛设计方法(DFSS,Design For Six Sigma)对微混系统中的微混控制器(即电力电子模块)进行优化设计,以满足在成本、散热及外形尺寸等方面的要求。运用六西格玛方法论的系统化的工作流程和相应工具,对微混控制器的设计通过需求分析、因子筛选、建模、仿真、数模分析等步骤,最后得出最优化设计。该方法适用于复杂系统的前期设计开发阶段,有助于稳健设计、减少开发成本。     

关于虚公差问题的研究

提出虚公差概念,指出虚公差的意义。在虚公差概念的基础上提出解算带补偿件的尺寸链的概率法,解释了国际GB5847－86为什么对有补偿件的尺寸链不推荐“概率法”的原因。从理论上证明了虚公差概念,对于带补偿件的尺寸链能用概率法来计算,从而为计算机辅助设计装配尺寸链提供了理论依据,同时大大简化了计算机辅助设计装配尺寸链。     

Design for Six Sigma

The concept of implementation of Six Sigma methodology was pioneered at Motorola in the 1980s with the aim of reducing quality costs. Six Sigma methodology has evolved into a statistically oriented approach to process, product or service quality improvement. It is a business performance improvement strategy used to improve profitability, to drive out waste in business processes and to improve the efficiency of all operations that meet or exceed customers' needs and expectations. A performance level of Six Sigma equates to 3-4 defects per million opportunities, where sigma is a statistical measure of the amount of variation around the process average. The average sigma level for most companies is three sigma. The authors offer guidance as to how companies may achieve Six Sigma performance. Organisations that have adopted the principles and concepts of Six Sigma methodology have realised that once they have achieved Five Sigma quality levels the only way to surpass the Five Sigma quality level is to redesign their products, processes and services from scratch. These circumstances have led to the development of what we call today 'design for Six Sigma'. Design for Six Sigma is a powerful approach to designing products, processes and services in a cost-effective and simple manner, to meet the needs and expectations of the customer     

Process improvement through Six Sigma with Beta correction: a case study of manufacturing company

This article discusses the successful implementation of Six Sigma DMAIC (Define–Measure–Analyse–Improve–Control) methodology along with Beta correction technique in an automotive part manufacturing company. The implementation of Six Sigma approach resulted in reduction of process capability-related problems and improved the first pass yield from 94.86 % to 99.48 %. After studying the baseline performance of the process, a brainstorming session was conducted with all stakeholders of the process for identifying the potential causes of the problem. Data were collected on all the identified potential causes and various statistical analyses like regression analysis, hypothesis testing, and Taguchi methods were performed for identifying the root causes. Solutions were identified and implemented for the validated root causes, and results were observed. The Beta correction technique was introduced for monitoring the process in the control phase. Implementation of Six Sigma methodology with Beta correction technique had a significant financial impact on the profitability of the company. An approximate saving of US$87,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. This study contributes uniquely by elucidating the synergistic impact of Beta correction for greater effectiveness of Six Sigma programmes in the engineering industry.