正交试验设计的矩阵分析方法

介绍了一种正交试验设计的矩阵分析方法,通过该方法能够计算出各因素各水平对试验结果影响的权重,根据权重的大小可以确定正交试验的最优方案以及各个因素对正交试验的指标值影响的主次顺序.     

橡胶输送带复盖胶配方的优化

橡胶配方的优化是一个多因素多指标的复杂课题,运用正交试验设计方法进行配方设计,将通过试验获得的数据藉助微机进行回归分析和优化计算,从而取得最优配方,是橡胶企业提高经济效益的一种较有效的方法. 浙江省某橡胶厂生产的橡胶输送带,年产量在100万平方米以上.在生胶与各种化工原料竞相提价,产品市场竞争激烈的环境中,企业只有加强质量管理,不断提高产品质量并降低成本,才能得以生存和发展.橡胶输送带中耗生胶量最多的部件是复盖胶层,占产品总耗胶量的55%,而该部件胶料质量要求高,成本也较大. 为了获得配方中各成份的改变对复盖胶各项物理…     

基于正交试验设计的Ω焊缝焊接工艺参数的确定

为了确定CRDM焊接机焊接工艺参数,应用正交试验设计法进行了Ω焊缝焊接工艺评定试验,用数理统计方法分析了对焊缝质量产生影响的各焊接参数的主次顺序,得到了最优生产条件。     

正交试验层次分析法

提出了一种正交试验数据分析的新方法——正交试验层次分析法(AnalyticHierarchyProcess,简称AHP).应用层次分析(AHP)对不考虑交互作用的正交试验进行统计分析,得出各因素各水平对试验结果的影响权重,从而确定因素的主次顺序及最佳条件.实验结果表明,应用AHP对正交试验进行分析得到的结论与传统的直观分析法一致.     

导弹武器射程评估的多因素正交试验设计方法

"问题驱动"式的教学理念、教学模式和方法是教学改革和发展的趋势.以导弹武器射程鉴定与评估为背景,探讨了正交试验设计的课程教学在其中的应用.通过对影响导弹武器射程因素的分析,在确定的水平条件下,进行正交试验设计,得到较好的组合方式.从理论分析和仿真案例,回答了多因素水平下的导弹射程试验评估问题.通过"问题驱动",不仅有利于提高学生学习兴趣和积极性,同时还有利于加深对理论和方法的认识和理解.     

基于正交试验的菜椒产量影响因素的分析

为了找出影响菜椒产量的主要因素及各因素的最佳取值水平,帮助菜农更好的科学安排生产,本文通过正交试验设计方法以甘肃省张掖市甘州区长安乡郭家堡村2008年菜椒的产量数据为例对影响菜椒产量的因素做出了分析,并得出了各因素的最佳取值水平为施肥量140kg/亩,苗期为60天,栽培密度为4250株/亩的重要结论.     

非正交的数据处理

一、问题的提出 在试验设计中最常用的设计是正交设计,正交设计不仅效率高而且计算简单。然而在试验过程中改变原设计方案也是常有的事,例如在原正交方案中增加一些试验点或减少一些试验点。这时所得的数据就是一组非正交的数据。 本文在正交数据基础上讨论非正交数据的处理问题,给出两种方法,大大简化了非正交数据的处理,并为开拓某些新的非正交设计提供分析方法。     

基于正交试验法和数值仿真的环形降压槽结构优化

降压槽存在相互矛盾的阻力和粘损特性均受各结构参数的影响,合理的结构参数选择一直是降压槽设计的难点问题.环形降压槽结构优化设计涉及到多因素多水平问题,数值仿真和正交试验设计方法相结合可以有效地减少对实验的依赖和试验次数.对环形降压槽进行结构参数化建模,建立了幂律流体在降压槽环空流道流动的数值仿真模型,可完成不同结构降压槽性能的数值仿真分析;制定了降压槽3因素3水平的多指标正交试验,采用极差和方差分析方法对试验的结果进行了分析,并用综合平衡的方法确定最优的降压槽结构参数组合.最终得到了各结构参数对降压槽的粘损和阻力性能的影响规律以及最优的结构参数,可为环形降压槽的设计和改进提供有价值的参考.     

正交试验设计方法在汽车制动系统试验设计中的应用

在新产品开发过程中，人们常需要了解各种因素对产品或产品的性能、成本、产量等指标的影响。实际问题中影响这些指标的因素往往很多，要想完成一个全因素组合配置试验以期了解这些影响常常是极浪费和不现实的。本结合HFC1061液压制动系统试验设计与分析的过程，总结正交试验设计在应用过程中影响因素及其位级规范化、试验设计和数据分析等几个主要步骤的实施方法与要求。     

应用正交试验确定环已醇最佳工艺条件

应用正交试验确定环已醇最佳工艺条件．本文采用正交试验的方法，对锦西化工总厂环已酮车间利用环已醇脱氢生产环已酮的生产工艺过程进行了正交试验设计，应用方差分析方法得出了最佳生产工艺条件，同时给出了经济效益分析评价     

产品设计与质量控制的动态因素模型(上)

本文介绍产品设计与质量控制的新思想,从产品的设计、生产和使用的角度来研究影响产品质量的所有因素,把它们看成是一个随机过程,提出了动态因素产品设计与质量控制的数学模型。文中把过程能力指数的要领推广到了特性能力指数函数,用于统一地来衡量产品的设计质量、生产质量和使用质量;给出了以特性能力指数函数为变量的产品动态质量损失和产品技术损失的概念,并为组成产品的原材料或零部件达到一致寿命的最经济设计提供了一种理论和方法。     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

Improving the accuracy of expert predictions of the future success of new internet services

One of the challenging issues in new product development is the evaluation of the future success of new product initiatives and the elimination of those with unpromising futures. The difficulty of assessing the future success of a new offering is more highlighted for new services than is for new products because of the unique characteristics of services. Earlier studies suggested that a number of factors can affect the quality of evaluating the future success of new products or services. Building on previous studies, this paper offers the first empirical tests of the roles of two important factors (task structuring and expertise sharing) in improving the quality of expert predictions of the future success of new Internet services. It presents an empirical study based on a longitudinal survey that involves both the initial predictions and the actual results for two Internet services to test related hypotheses. The results showed that these factors can significantly improve the predictive accuracy of experts in evaluating new Internet services. More importantly, the results also indicated a significant substitution effect between the factors, suggesting that either of these factors is sufficient to make major improvements in the accuracy of the evaluations. Because the factors can largely substitute for each other; however, using both can lead to only minor improvements over and above using just one.     

多元质量特性预报：MULTIVARIATE回归分析的应用

对现象之间客观存在的因果关系建立回归分析模型,这是实际中较为普遍的做法.在这篇文章中,我们根据MULTIVARIATE回归分析的基本原理,利用从生产现场采集的观测数据,对产品两个质量特性及其五个关键影响因素之间的关系建立了多重多元回归分析方程,为说明MULTIVARIATE回归应用的可行性,我们还结合实例给出了因变量向量估计的两种形式,以及无条件预报的置信区间。     

Worst-Case Tolerance Design and Quality Assurance via Genetic Algorithms

In many engineering designs, several components are often placed together in a mechanical assembly. Due to manufacturing variations, there is a tolerance associated with the nominal dimension of each component in the assembly. The goal of worst-case tolerance analysis is to determine the effect of the smallest and largest assembly dimensions on the product performance. Furthermore, to achieve product quality and robustness, designers must ensure that the product performance variation is minimal.Recently, genetic algorithms (GAs) have gained a great deal of attention in the field of tolerance design. The main strength of GAs lies in their ability to effectively perform directed random search in a large space of design solutions and produce optimum results. However, simultaneous treatment of tolerance analysis and robust design for quality assurance via genetic algorithms has been marginal.In this paper, we introduce a new method based on GAs, which addresses both the worst-case tolerance analysis of mechanical assemblies and robust design. A novel formulation based on manufacturing capability indices allows the GA to rank candidate designs based on varying the tolerances around the nominal design parameter values. Standard genetic operators are then applied to ensure that the product performance measure exhibits minimal variation from the desired target value. The computational results in the design of a clutch assembly highlight the advantages of the proposed methodology.     

基于产品质量控制的设备维修优化模型

根据产品质量和生产该产品的设备退化状态之间的相关性,设计了周期性设备检测与产品质量控制相结合的设备维修策略。该策略是在对设备进行周期性检测的基础上,利用控制图进行产品质量异常波动的检测,结合对设备退化状态的检测选择设备应采取的维修活动。根据这一设备维修策略,利用更新过程理论和统计过程控制方法,构建了基于产品质量控制的设备维修优化模型,并用遗传算法对其进行求解。通过实例仿真验证了该模型的可行性与有效性。     

A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis

Sustainable product design has been considered as one of the most important practices for achieving sustainability. To improve the environmental performances of a product through product design, however, a firm often needs to deal with some difficult technical trade-offs between traditional and environmental attributes which require new design concepts and engineering specifications. In this paper, we propose a novel use of the two-stage network Data Envelopment Analysis (DEA) to evaluate sustainable product design performances. We conceptualize “design efficiency” as a key measurement of design performance in terms of how well multiple product specifications and attributes are combined in a product design that leads to lower environmental impacts or better environmental performances. A two-stage network DEA model is developed for sustainable design performance evaluation with an “industrial design module” and a “bio design module.” To demonstrate the applications of our DEA-based methodology, we use data of key engineering specifications, product attributes, and emissions performances in the vehicle emissions testing database published by the US EPA to evaluate the sustainable design performances of different automobile manufacturers. Our test results show that sustainable design does not need to mean compromise between traditional and environmental attributes. Through addressing the interrelatedness of subsystems in product design, a firm can find the most efficient way to combine product specifications and attributes which leads to lower environmental impacts or better environmental performances. This paper contributes to the existing literature by developing a new research framework for evaluating sustainable design performances as well as by proposing an innovative application of the two-stage network DEA for finding the most eco-efficient way to achieve better environmental performances through product design.     

Profit oriented supply chain network optimization

This paper proposes a novel mixed integer linear programming model to solve a supply chain network design problem. The proposed model deals with major issues for supply chains; product quality and cost. These issues are usually solved separately, but in this paper, we investigate effects of product quality on supply chain design and transportation flow. A trade-off between raw material quality, its purchasing and reprocessing costs was considered. Assuming decision maker (DM) wishes to work with a supplier which serves a low quality raw material; this raw material should be in need of reprocessing. To avoid the reprocessing costs, a supplier which serves a high quality raw material should be chosen but at this time the DM has to face a high purchasing cost. A supply chain network which consists of multiple suppliers, manufacturers, distribution centers and retailers is tried to be designed to accomplish aforementioned above trade-offs. The paper examines and discusses the relationship between product quality and supply chain design and offers several managerial insights.     

A demonstration of the utility of fractional experimental design for finding optimal genetic algorithm parameter settings

This paper demonstrates that the use of sparse experimental design in the development of the structure for genetic algorithms, and hence other computer programs, is a particularly effective and efficient strategy. Despite widespread knowledge of the existence of these systematic experimental plans, they have seen limited application in the investigation of advanced computer programs. This paper attempts to address this missed opportunity and encourage others to take advantage of the power of these plans. Using data generated from a full factorial experimental design, involving 27 experimental runs that was used to assess the optimum operating settings of the parameters of a special genetic algorithm (GA), we show that similar results could have been obtained using as few as nine runs. The GA was used to find minimum cost schedules for a complex component assembly operation with many sub-processes.     

广义正交表和正交表的裂区试验设计法的比较

裂区试验设计方法是在正交表的基础上进行的.根据试验设计的数据分析结论要求具有再现性这一原理,将证明这种裂区试验设计法要有条件的使用才是合理的.由于广义正交表是保证设计表具有再现性的基本设计表,根据广义正交表来研究这种裂区试验设计方法的合理性.研究结果显示在裂区试验设计法对应的设计表是广义正交表,并且相应的数据分析方法采用广义正交表的数据分析方法时,才能保证其数据分析结论具有客观一致性和可重复再现性.