Proposal of variable sequential sampling plan having desired operating characteristics indexed by quality loss

The proportion of non-conforming items has been traditionally utilised as an evaluation criterion for quality of items. However, the proportion of non-conforming items is not necessarily useful as a proper evaluation criterion for controlling high-quality manufacturing in recent years. Accordingly, in order to achieve further quality improvement and innovation, more careful quality evaluation has been required newly. Then, a concept of quality loss in the Taguchi methods has been devised as a severe criterion of quality evaluation. Hereby, a variable single sampling plan having desired operating characteristics (OCs) indexed by quality loss has been proposed in the area of statistical quality control. By the way, the most economical sampling inspection in the average sample number (ASN) is the sequential sampling plan based on the Wald’s sequential probability ratio test. Then, from the viewpoint of cost reduction, we discuss a variable sequential sampling plan having desired OC indexed by quality loss with the aim of expansion of the utility of variable sampling plan for quality loss. As the result, the design procedure of the sequential sampling plan for satisfying some required design conditions indexed by quality loss is provided. In addition, the effectiveness of the proposed sequential sampling plan is verified through some numerical examples.     

Automated sampling decision scheme for the AVM system

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic virtual metrology based intelligent sampling decision (ISD) scheme had been previously developed for reducing the sampling rate and sustaining the virtual metrology (VM) accuracy. However, the desired sampling rate of the ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot adaptively increase the default sampling rate in the ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the default sampling rate in ISD, which may result in unnecessary waste. Accordingly, this paper proposes an automated sampling decision (ASD) scheme to adaptively and automatically modify the sampling rate online and in real time for continuous improvement. The ASD scheme can monitor the VM accuracy online as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the ASD scheme can automatically reduce the sampling rate while the VM accuracy performs well.     

A deterministic sequential maximin Latin hypercube design method using successive local enumeration for metamodel-based optimization

Space-filling and projective properties of design of computer experiments methods are desired features for metamodelling. To enable the production of high-quality sequential samples, this article presents a novel deterministic sequential maximin Latin hypercube design (LHD) method using successive local enumeration, notated as sequential-successive local enumeration (S-SLE). First, a mesh-mapping algorithm is proposed to map the positions of existing points into the new hyper-chessboard to ensure the projective property. According to the maximin distance criterion, new sequential samples are generated through successive local enumeration iterations to improve the space-filling uniformity. Through a number of comparative studies, several appealing merits of S-SLE are demonstrated: (1) S-SLE outperforms several existing LHD methods in terms of sequential sampling quality; (2) it is flexible and robust enough to produce high-quality multiple-stage sequential samples; and (3) the proposed method can improve the overall performance of sequential metamodel-based optimization algorithms. Thus, S-SLE is a promising sequential LHD method for metamodel-based optimization.     

A Single Sampling Plan Based on Exponentially Weighted Moving Average Model for Linear Profiles

The exponentially weighted moving average (EWMA) model has been successfully used in acceptance sampling plans. The EWMA model provides the quality information of the current lot and the preceding lots. In addition, a multiple dependent state (MDS) sampling plan considers the quality information of the preceding lots. In this study, we present two new sampling plans for linear profiles. One is based on EWMA model with yield index using the single sampling plan, and the other is based on EWMA model with yield index using the MDS sampling plans. The plan parameters are determined by a nonlinear optimization approach. As the smoothing parameter value equals to one, the first proposed plan becomes the traditional single sampling plan. In addition, we compare the proposed plans with the traditional single sampling plan. The results indicate that the MDS sampling plan based on EWMA model with yield index with smaller value of smoothing parameter performs better than the traditional single sampling plan and the single sampling plan based on EWMA model with yield index in terms of the sample size required. One real example is used to illustrate the proposed plan. Copyright © 2015 John Wiley & Sons, Ltd.     

空间站展开机构的失谐动态响应分析

考虑了动力失谐、质量失谐和摩擦失谐三种常见失谐现象,研究了空间站展开机构的失谐动态响应。利用多柔体系统动力学方法,在机械系统动力学自动分析(ADAMS)仿真平台下建立了空间站柔性展开机构的失谐模型,并用重要度抽样方法随机抽取失谐量,然后进行机构动学仿真,得到机构的失谐动态响应并进行统计。仿真研究证明,该方法可用较少的计算时间得到柔性展开机构的失谐动态响应随机分布特性。     

A new methodology for Bayesian history matching using parallel interacting Markov chain Monte Carlo

This paper presents an innovative application of a new class of parallel interacting Markov chains Monte Carlo to solve the Bayesian history matching (BHM) problem. BHM consists of sampling a posterior distribution given by the Bayesian theorem. Markov chain Monte Carlo (MCMC) is well suited for sampling, in principle, any type of distribution; however the number of iteration required by the traditional single-chain MCMC can be prohibitive in BHM applications. Furthermore, history matching is typically a highly nonlinear inverse problem, which leads in very complex posterior distributions, characterized by many separated modes. Therefore, single chain can be trapped into a local mode. Parallel interacting chains is an interesting way to overcome this problem, as shown in this paper. In addition, we presented new approaches to define starting points for the parallel chains. For validation purposes, the proposed methodology is firstly applied in a simple but challenging cross section reservoir model with many modes in the posterior distribution. Afterwards, the application to a realistic case integrated to geostatistical modelling is also presented. The results showed that the combination of parallel interacting chain with the capabilities of distributed computing commonly available nowadays is very promising to solve the BHM problem.     

CATIA V5环境下基于产品数模的三坐标测量机测点自动采样

为了实现CAD与CAIP(计算机辅助测量规划)的集成,提出了一种基于产品三维模型的CMM(三坐标测量机)测量点采样方法。该方法从产品的IGES文件中有效识别型面形状与边界定义,采用均匀采样与依曲率变化二次采样相结合的策略,针对各个型面类型制定专门的信息处理机制,实现测量点的自动采样,并通过转换在参数平面中判断采样点与裁剪区域的隶属关系,以有效剔除无效采样点;通过典型飞机部件外形曲面的采样点生成结果表明,该方法能有效应用于飞机的测量规划中。     

Efficient conditional probability theorem and importance sampling-based methods for global reliability sensitivity analysis

Global reliability sensitivity (GRS) analysis can measure the effect of random inputs on failure probability (FP). To efficiently solve GRS, two conditional probability theorem (CPT)-based methods are proposed by combining adaptive Kriging (AK) with importance sampling (IS) (CPT-AK-IS) and combining AK with Meta-IS (CPT-AK-Meta-IS) respectively. Firstly, differentiation approximation and CPT are used to convert the estimation of conditional probability density function (PDF), which is required by the existing Bayes theorem-based methods, into that of a series of probabilities. Secondly, GRS can be directly estimated by the failure samples of IS, while the existing Bayes theorem methods based on IS need to transform the failure samples of IS into those of original PDF. Both the first and second strategies can reduce the computational complexity of solving GRS. Thirdly, by selecting a suitable differentiation interval with a proposed adaptive strategy, the estimation of a series of probabilities can be accurately completed as a byproduct of one IS based simulation for solving FP without additional computational cost. Finally, by introducing AK into IS and Meta-IS, it can reduce the number of evaluating performance function and the size of candidate sample pool simultaneously. These novelties are sufficiently verified by the presented examples.     

嵌入式冷库环境监控系统的设计

为了保证冷库内货物质量、维持其最佳储藏环境、提高冷库自动化水平,设计了一款冷库智能监控系统;系统以LPC2103为核心,利用传感器、信号调理电路进行数据采集,并结合软件实现对冷库环境参数的控制;分布式采样点的硬件布局克服了冷库环境参数不均匀,波动性大的缺陷,通过软件滤波算法与智能控制方法进一步屏蔽了信号传输的干扰,为控制设备提供了准确的指令;对温度参数进行记录与分析的实验结果表明,系统误差小并能够准确、及时的调节环境,因此系统结构简单,运行稳定,调控方便,能够有效保证储藏货物质量.