CCC‐r charts' performance with estimated parameter for high‐quality process

CCC‐r charts are effective in detecting process shifts in the nonconforming rate especially for a high‐quality process. The implementation of the CCC‐r charts is usually under the assumption that the in‐control nonconforming rate is known. However, the nonconforming rate is never known, and accurate estimation is difficult. We investigate the effect of estimation error on the CCC‐r charts' performances through the expected value of the average number of observations to signal (EANOS) as well as the standard deviation of the average number of observations to signal (SDANOS). By comparing the in‐control performance of the CCC‐r charts, the CCC‐r chart with a larger value of r is more susceptible to the effects of parameter estimation. Meanwhile, the performance of the CCC‐r charts can converge when detecting upward shifts in p of out‐of‐control processes. We recommend the use of the CCC‐4 chart when considering its effectiveness in detecting shifts as well as its easier construction in practice. Furthermore, it is investigated that the CCC‐4 chart is less sensitive to parameter estimation while being more effective in detecting different process shifts when compared with Geometric CUSUM chart and synthetic chart.     

Removal Efficacy of Opportunistic Pathogens and Bacterial Community Dynamics in Two Drinking Water Treatment Trains

Drinking water treatment processes (DWTPs) impact pathogen colonization and microbial communities in finished water; however, their efficacies against opportunistic pathogens are not fully understood. In this study, the effects of treatment steps on the removal of Legionella spp., Legionella pneumophila, nontuberculous mycobacteria, Mycobacterium avium, and two amoeba hosts (Vermamoeba vermiformis, Acanthamoeba) are evaluated in two parallel trains of DWTPs equipped with different pretreatment units. Quantitative polymerase chain reaction analysis demonstrates significantly reduced numbers of total bacteria, Legionella, and mycobacteria during ozonation, followed by a rebound in granular activated carbon (GAC) filtration, whereas sand filtration exerts an overarching effect in removing microorganisms in both treatment trains. V. vermiformis is more prevalent in biofilm (34%) than water samples (7.7%), while Acanthamoeba is not found in the two trains of DWTPs. Illumina sequencing of bacterial 16S rRNA genes reveals significant community shifts at different treatment steps, as well as distinct bacterial community structures in water and biofilm samples in parallel units (e.g., ozonation, GAC, sand filtration) between the two trains (analysis of similarities (ANOSIM), p < 0.05), implying the potential influence of different pretreatment steps in shaping the downstream microbiome. Overall, the results provide insights to mitigation of opportunistic pathogens and engineer approaches for managing bacterial communities in DWTPs.     

A metal marking method based on laser shock processing

The stress effect generated by a high-energy short-pulse laser is utilized to achieve performance enhancement or precise shaping of metal material components in laser shock technology. A method of marking metal materials using laser shock technology is introduced in this paper. The physical principle is the effect of thickness of absorbing layers on the intensity of a laser-induced shockwave, which results in forming the mark with a certain shape using local surface plastic deformation. The marking method based on the principle of laser shock overcomes the deficiency of traditional marking methods that causes serious damage to the surface of the metal material and leads to a decrease in service performance. Laser shock marking(LSM) allows the surface of the metal material to form a predetermined mark, which also improves the service performance accordingly.     

Nanostructured Metal–Organic Conjugated Coordination Polymers with Ligand Tailoring for Superior Rechargeable Energy Storage

Conjugated coordination polymers have become an emerging category of redox‐active materials. Although recent studies heavily focus on the tailoring of metal centers in the complexes to achieve stable electrochemical performance, the effect on different substitutions of the bridging bonds has rarely been studied. An innovative tailoring strategy is presented toward the enhancement of the capacity storage and the stability of metal–organic conjugated coordination polymers. Two nanostructured d‐π conjugated compounds, Ni[C₆H₂(NH)₄]_n (Ni‐NH) and Ni[C₆H₂(NH)₂S₂]_n (Ni‐S), are evaluated and demonstrated to exhibit hybrid electrochemical processes. In particular, Ni‐S delivers a high reversible capacity of 1164 mAh g^?1, an ultralong stability up to 1500 cycles, and a fully recharge ability in 67 s. This tailoring strategy provides a guideline to design future effective conjugated coordination‐polymer‐based electrodes.     

适用于机器人焊接的搅拌摩擦焊技术及工艺研究现状

集绿色化与智能化于一体的机器人搅拌摩擦焊技术具有极高的焊接柔性,在工业生产中受到广泛重视与应用。从减小搅拌摩擦焊接过程中的轴向力方面入手,对一些低轴向力且适用于机器人搅拌摩擦焊的技术方法进行了综述,同时,对能够进一步降低搅拌摩擦焊过程中轴向力的搅拌摩擦焊工艺进行了分析,并对机器人搅拌摩擦焊的发展进行了展望,以期拓宽机器人搅拌摩擦焊的应用范围。     

两参数跳过程三点转移函数的连续与正则性

利用单参数跳过程已有结论，讨论了两参数跳过程三点转移函数的连续性，在坐标轴上的极限．     

n－参数Ornstein—Uhlenbeck过程的导出

证明了ＯＵｐｎ在射线上导出的过程为ＯＵｐｎ的充要条件，并求出了ＯＵｐｎ的截口过程的单点转移函数和宽过去转移函数．     

Bivariate Dispersion Control Charts for Monitoring Non‐Normal Processes

Multivariate control charts are well known to be more sensitive to the occurrence of variation in processes with two or more correlated quality variables than univariate charts. The use of separate univariate control charts to monitor multivariate process can be misleading as it ignores the correlation between the quality characteristics. The application of multivariate control charts allows for the simultaneous monitoring of the quality characteristics by forming a single chart. The charts operate on the assumption that process observations are normally distributed, but in practice this is not always the case. In this study, we examine and present multivariate dispersion control charts for detecting shifts in the covariance matrix of normal and non‐normal bivariate processes. These control charts, referred to as SMAX, QMAX, MDMAX and MADMAX, rely on dispersion estimates, such as the sample standard deviation (S), interquartile range (Q), average absolute deviation from median (MD) and median absolute deviation (MAD), respectively. We compare the performances of these charts to the existing multivariate generalized variance |S| and RMAX charts for bivariate processes using normal and non‐normal parent distributions. The average run length (ARL) measure is used for the evaluation and comparison of the charts. A real life and simulated datasets are used to demonstrate the application of the charts. Copyright © 2016 John Wiley & Sons, Ltd.     

Does theory work in practice? Two case studies

ABSTRACT

Case studies offer an opportunity to review what worked and what didn't and to draw conclusions about what changes to make in future projects. This article presents two different studies. Each study explored the properties of a production process and each had a number of issues to be resolved before experimental runs could be performed. In the first case, the process was a continuous rubber extrusion line, producing windscreen wiper blades. Planning involved people on three different continents, so issues of building trust were paramount. Only a narrow time window was available for experimentation, so flexibility and a quick response to problems as they arose were needed. The second study was an off-line batch process aimed at producing polymers suitable for artificial corneas. There were two competing variables of interest. Previous attempts to improve the product had been piecemeal and unsuccessful, but a fractional factorial experiment provided guidance on a way forward. Subsequent runs then aimed to optimize the primary variable whilst holding the second variable constant. By comparing and contrasting these studies, many valuable lessons can be learned.     

Monitoring bivariate and trivariate mean vectors with a Shewhart chart

In this article, we propose the use of the mean chart to control multivariate processes. The basic idea is to control the mean vector of bivariate (X, Y) and trivariate (X, Y, Z) processes by alternating the charting statistic of the Shewhart chart. If the mean of X observations was the charting statistic to obtain the current sample point, then the mean of Y observations will be the charting statistic to obtain the next sample point (for the trivariate case, the mean of Z observations will be the charting statistic to obtain the sample point subsequent to the next one). As a Shewhart chart, the signal is given anytime a sample point is plotted beyond the control limits, independent of the charting statistic in use. A fair comparison between the proposed chart and the Hotelling chart is based on an equal number of measurements per sample. The Shewhart chart with alternated charting statistic (ACS) always outperforms the Hotelling chart, except for specific types of disturbances in quality characteristics highly correlated (ρ = 0.7). The ACS chart is substantially easier to operate and faster than the Hotelling chart in signaling changes in the mean vector of bivariate and trivariate processes. Even with fewer measurements per sample, the trivariate ACS chart outperforms the Hotelling chart.