Acceptance sampling plan for multiple manufacturing lines using EWMA process capability index

The problem of developing a product acceptance determination procedure for multiple characteristics has attracted the quality assurance practitioners. Due to sufficient demands of consumers, it may not be possible to deliver the quantity ordered on time using the process based on one manufacturing line. So, in factories, product is manufactured using multiple manufacturing lines and combine it. In this manuscript, we present the designing of an acceptance sampling plan for products from multiple independent manufacturing lines using exponentially weighted moving average (EWMA) statistic of the process capability index. The plan parameters such as the sample size and the acceptance number will be determined by satisfying both the producer's and the consumer's risks. The efficiency of the proposed plan will be discussed over the existing sampling plan. The tables are given for industrial use and explained with the help of industrial examples. We conclude that the use of the proposed plan in these industries minimizes the cost and time of inspection. Smaller the sample size means low inspection cost. The proposed plan for some non-normal distributions can be extended as a future research. The determination of sampling plan using cost model is also interested area for the future research. ? 2017 The Japan Society of Mechanical Engineers.11Ysciescopu

Statistical Quality Control with the qcr Package

[Abstract] The R package qcr for Statistical Quality Control (SQC) is introduced and described. It includes a comprehensive set of univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC (qcc, MSQC), qcr provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, (Formula presented), S, R, p, np, c, u, EWMA, and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T2, MEWMA and MCUSUM. As representative features, multivariate nonparametric alternatives based on data depth are implemented in this package: r, Q and S control charts. The qcr library also estimates the most complete set of capability indices from first to the fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. Moreover, Phase I and II control charts for functional data are included.The work of Salvador Naya, Javier Tarrío-Saavedra, Miguel Flores and Rubén Fernández-Casal has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. The research of Miguel Flores has been partially supported by Grant PII-DM-002-2016 of Escuela Politécnica Nacional of Ecuador. In addition, the research of Javier Tarrío-Saavedra has been also founded by the eCOAR project (PC18/03) of CITICXunta de Galicia; ED431C-2020-14Xunta de Galicia; ED431G 2019/01Escuela Politécnica Nacional de Ecuador; PII-DM-002-201

Combining Capability Indices and Control Charts in the Process and Analytical Method Control Strategy

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required

Detecting the process\u27 1.5 sigma shift: A quantitative study

Process behavior can change with time. In this study an attempt was made to discover whether the Six Sigma™ claim of changes in the process mean stayed within +/- 1.5 sigma units. Several process groups were examined for a particular firm that made metal castings, machined parts, tested major components and assembled these into a vehicle that was a product sold to the customer. As the assembly progressed, deficiencies were identified and recorded. Analyses employed cumulative sum (CUSUM) sequence charts, Autoregressive Integrated Moving Average (ARIMA) time series analyses, minimum mean square error (MMSE) exponentially weighted moving average (EWMA), Shewhart control charts and Analysis of Variance (ANOVA) to identify the shift in the process mean, M/sw, the duration of the shift, λB, and the proper choice of EWMA smoothing coefficient, λEWMA. Kruskal-Wallis analysis of the relationship of these measures to process group (assembly, foundry, heat treatment, machining, shaving, test machine, grinding, turning, warranty and yield) was also performed. The method used was generally applicable for all these processes. The process group and the ARIMA type also influenced the measurement of M/sw , λB , and λEWMA

Quality Assurance through Process Improvement - A Concise Review

The past two decades has seen the realization of the manufacturing firms towards quality consciousness. The various firms worldwide employed the statistical tools for minimizing the deviations and subsequently the number of rejects of the manufactured parts. In this process, more prominence is laid on prevention of defects rather than detecting and rejecting the defect in the usual traditional end inspection quality check. This review paper witnesses the procedures involved in the improvement of the manufacturing process capability and focuses on achieving quality assurance through sustained improvement in the manufacturing process. The different methodologies for sustained improvement in manufacturing process across varied industrial sectors, are identified, discussed and presented. The nuances of enhanced product quality levels through process capability improvement work performed in varied areas of industrial manufacturing are also captured

A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor

Life jacket

Anyone who cannot swim well should wear life jacket whether they are in the water or around the water. Even those who are can swim well should wear the life jacket when they are doing activity such as swimming, fishing, boating or while doing any water-related activity. Life jacket is a kind of safety jacket that keeping the wearer float the in the water. The wearer may be in the conscious or unconscious condition but by wearing the life jacket we can minimize the risk of drowning because life jacket assist the wearer to keep floating in the water

Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

Monitoring and performance analysis of regression profiles

There are many cases in industrial and non-industrial sections where the quality characteristics are in the form of profiles. Profile monitoring is a relatively new set of techniques in statistical quality control that is used in situations where the state of product or process is presented by regression models. In the past few years, most research in the field of profile monitoring has mainly focused on the use of effective statistical charting methods, study of more general shapes of profiles, and the effects of violations of assumptions in profile monitoring. Despite several research on the application of artificial neural networks to statistical quality control, no research has investigated the application of neural networks in monitoring profiles. Likewise, there is no research in the literature on the process capability analysis in profile processes. The process capability analysis is to evaluate the ability of a process to meet the customer/engineering specifications and must be done in Phase I of profile monitoring. In a review study on profile monitoring, Woodall (2007) pointed out the importance of process capability analysis in profiles. In this research, we use artificial neural networks (ANN) to detect and classify shifts in linear profiles. Three monitoring methods based on ANN are developed to monitor linear profiles in Phase II. We compare the results for different shift scenarios with existing methods in linear profile monitoring and discuss the results. Furthermore, in this thesis, we evaluate the estimation of process capability indices (PCIs) in linear profiles. We propose a method based on the relationship between proportions of non-conformance and the process capability indices in the profile process. In most existing profile monitoring methods in the literature, it is assumed that the profile design points are deterministic (fixed) so they are unchanged from one profile to another one. In this research, we investigate the estimation of the PCI in normal linear profiles for different scenarios of deterministic and arbitrary (random) data acquisition schemes as well as fixed or linear functional specification limits. We apply the proposed method in estimating the PCI in a yogurt production process. This thesis also focuses on the investigation of the process capability analysis in profiles with non-normal error terms. In this study, we review the methods for estimating PCI in non-normal data and carry out a comprehensive comparison study to evaluate the performance of these methods. Then these methods are applied in the leukocyte filtering process to evaluate the PCI with effect of non-normality in a blood service section. In addition, we develop a new method based on neural networks to estimate the parameters of the Burr XII distribution, which is required in some of the PCI estimation methods with non-normal environments. Finally, in this research we propose five methods to estimate process capability index in profiles where residuals follow non-normal distributions. In a comparison study using Monte Carlo simulations we evaluate the performance of the proposed methods in terms of their precision and accuracy. We provide conclusions and recommendation for the future research at the end

Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network

[[abstract]]In this paper artificial neural network (ANN) technologies and analytical models have been investigated and incorporated to increase the effectiveness and efficiency of machinery self diagnostic system. Several advanced vibration trending methods have been studied and used to quantify machine operating conditions. An on-line, multi-channel condition monitoring procedure has been developed and coded. The major technique used for self diagnostic is a modified ARTMAP neural network. The objective is to provide a rigid solution for condition-based intelligent self diagnostic system.[[conferencetype]]國際[[conferencedate]]20091010~20091011[[iscallforpapers]]Y[[conferencelocation]]Changsha, Huna