Joint Monitoring of Process Location and Dispersion Based on CUSUM Procedure and Generalized Likelihood Ratio in the Presence of Measurement Errors

In this paper, we propose a new methodology based on the combination of cumulative sum procedure and generalized likelihood ratio statistic for joint monitoring of the process location and dispersion. Then, we explore the effect of measurement errors on detecting ability of the proposed control chart when (i) the variance of measurement error is constant (ii) the variance of measurement error increases linearly as the level of the process mean increases. We also utilize multiple measurements on each sample point in order to decrease the adverse effects of measurement errors on the performance of the proposed control charts. Two numerical examples based on simulation studies are given to evaluate the ability of the proposed methods in terms of average run length, median run length, standard deviation of run length, and the first and third quantile points of the run length distribution (Q₁ and Q₃). Finally, a real life example is given to illustrate the application of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.     

On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

The effective recognition of unnatural control chart patterns (CCPs) is one of the most important tools to identify process problems. In multivariate process control, the main problem of multivariate quality control charts is that they can detect an out of control event but do not directly determine which variable or group of variables has caused the out of control signal and how much is the magnitude of out of control. Recently machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. This study presents a modular model for on-line analysis of out of control signals in multivariate processes. This model consists of two modules. In the first module using a support vector machine (SVM)-classifier, mean shift and variance shift can be recognized. Then in the second module, using two special neural networks for mean and variance, it can be recognized magnitude of shift for each variable simultaneously. Through evaluation and comparison, our research results show that the proposed modular performs substantially better than the traditional corresponding control charts. The main contributions of this work are recognizing the type of unnatural pattern and classifying the magnitude of shift for mean and variance in each variable simultaneously.     

Multiple response surface optimization with correlated data

Setting of process variables to meet the required specification of quality characteristics is an important problem in the process quality control. There are often several conflicts in quality characteristics, which should be simultaneously satisfied. These types of problems are called “Multiple Response Optimization” (MRO). When quality characteristics are correlated, MRO problems may become increasingly difficult. In design of experiments, identifying covariates effects could reduce error and uncovered variances as well as give more insight about the process. This study aims to identify process variables to consider correlated covariates and correlated quality characteristics. It also accommodates dispersion effects and specification limits as well as location effects in a unified framework based on desirability functions. The features of the proposed method are investigated and the results are compared with some existing techniques by applying two numerical examples.     

An approach to optimize correlated multiple responses using principal component analysis and desirability function

While researchers have developed several approaches to attain design variable settings that simultaneously optimize multiple-quality characteristics, the multi-response optimization has become a common practice in complicated manufacturing processes. Most of these research works assume independency of responses where their variances are constant over the experimental space. However, there are many manufacturing processes in practice where the quality characteristics under consideration are correlated. In this study, an efficient approach based on principal component analysis and a conventional desirability function is proposed to optimize correlated multiple responses. This approach not only obtains optimal operating conditions, but also considers different variance and correlation levels of responses and enforces all objectives to satisfy constraints. Experimental results obtained using a standard example show the effectiveness of the proposed method.     

An integration of NSGA-II and DEA for economic–statistical design of T2-Hotelling control chart with double warning lines

Control charts are the most applicable tools for monitoring the quality of processes. The day-to-day changes in industrial processes and customers’ expectations motivate the process engineers to monitor multiple correlated quality characteristics, simultaneously. Hence, in this paper, the design of a “double warning lines T²-Hotelling” control chart is studied because of the advantages of this multivariate control chart in detecting moderate and small shifts in a process. In this regard, this research aims to optimize a multi-objective economic–statistical design model that considers monitoring costs and statistical features of control chart, concurrently. The non-dominated sorting genetic algorithm II is utilized to obtain a suitable Pareto set for the model. Since it is difficult for the decision makers to select the most efficient solution among the Pareto set, three different methods of data envelopment analysis consisting of Charnes–Cooper–Rhodes model, cross-efficiency technique and aggressive formulation are used to rank the members of Pareto set and to select the most efficient one. Also, in this research the performance of these three methods in discriminating between the efficient solutions is compared to each other. Eventually, a comparative study is conducted to show the better performance of the suggested model in comparison with the corresponding economic design model.

     

A robust interactive approach to optimize correlated multiple responses

Setting of process variables to meet the required specification of quality characteristics is a common problem in the process quality control. To obtain the most satisfactory solution, a decision maker's (DM) preference information on the trade-offs among the quality characteristics should be incorporated into the optimization procedure. In this regard, several multiple response surface optimization (MRO) techniques have been proposed in recent years. Most of these techniques require that all the preference information is specified in advance which is very difficult in practice. Furthermore, most of them assume independency of quality characteristics where their variances are constant over the experimental space. An interactive approach to optimize multiple responses is presented that does not require any information about DM's preference before solving process. This method aims to identify process variables to consider correlation among quality characteristics and minimize the variation in deviation of responses from their targets. It also accommodates dispersion effects and specification limits as well as location effects in a unified framework based on desirability functions. The features of the proposed method are investigated and the results are compared with some existing techniques through a real numerical example. Obtained results indicate the superiority of proposed methodology with respect to the major existing MRO techniques.     

A robust scheduling of projects with time, cost, and quality considerations

CAM programs can generate cutting tool paths to be used by machining centres. Experience shows that CAM programmed feed rates are rarely achieved in practice during machining, especially when finishing free-form surfaces. These slower feed rates are due to the machines’ kinematic capabilities and behaviour of the numerical control (NC). To improve control over the machining process, applications need to be developed to predict the kinematic behaviour of the machines, taking the mechanical characteristics of the axes and NC capacities into account. Various models to simulate tool paths in linear and circular interpolation have been developed and are available in the literature. The present publication will first focus on the use of the polynomial model to simulate the behaviour of the machine when passing through transitions between programmed blocks with tangency discontinuities. Additional features are proposed to ensure enhancement of the match between the model and the machine’s behaviour. Analysis of machine behaviour shows that NCs do not always allow the axes to reach maximum performance levels, with an attendant loss in productivity. The present article proposes an optimisation procedure allowing control laws to be defined to reduce time spent in the transition. The contributions made by these optimised control laws are then evaluated, while impediments to their implementation are also considered.