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There is well known: whether the advantage of using a normal process model to monitor the stability of a manufacturing process can be gained lies in the model's ability used to realize its conformance to the manufacturing process trend. In other words, whether a manufacturing process can be stabilized depends on how much is about the conformance level between its processing trend and the norm regulated...
Product quality plays an important role in facing competition and gaining competitiveness. Both Engineering Process Controllers (EPC) and Statistical Process Control (SPC) are effective methods of monitoring and adjusting the transition stages to improve process quality. At the same time, neural network was adopted to monitor the process and a flexible model is developed to determine optimal adjustable...
Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-of-control signal of a multivariate chart. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous...
Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as T2, multivariate cumulative sum (MCUSUM)...
In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both...
In some practical situations, the quality of a process or product is characterized by a relationship (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we propose a supervised feed forward neural network to detect and classify drift shifts in linear profiles. The proposed method contains...
To solve the problems of "window of opportunity" and autocorrelation under the integrated scheme of statistical process control (SPC) and engineering process control (EPC), a compositive method of neural networks and conventional SPC techniques was presented in this paper. Neural networks technique was used to monitor the process output, while EWMA chart and Shewart chart were adopted to...
The statistical process control (SPC) chart is effective in detecting process shifts. One important assumption for using the traditional SPC charts requires that the plotted observations are independent to each other. Otherwise, the so called "false alarm" would be increased, and these improper signals result in the wrong interpretation. However, this independent assumption is often not...
An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN...
Aimed at an integration system of SPC (statistical process control) and APC (automatic process control), the effect of APC on SPC detection capability is analyzed. In this paper, the process disturbance is assumed to be an ARMA (1, 1) process. BP Neural Network with good capability of mode identification is used to substitute traditional SPC technology. Then an integrated design methodology has been...
Statistical process control can have different objectives and can be done in different forms (Hawkins, et al, 2003). Currently, considerable attention has been given to the effect of data correlation on the statistical process control (SPC). The use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. This...
Many of the previous research on the control chart pattern recognition were related to fully developed patterns. However, in practice, the process data will appear as a continuous stream of partially developed patterns. Such developing patterns are difficult to recognize since their structure are normally vague and dynamic. This study investigated the merit of a generalized single recognizer (all-class-one-network,...
Quality control charts have proven to be very effective in detecting out of control signals. It is very important to practitioners to determine at what point in the past the signal was initiated. If a control chart signals a change in the process parameter, identifying the time of the change will substantially help the signal diagnostics procedure since it simplifies the search for special causes...
This research investigated the performance of the X̄ chart when the heterogeneous variation occurs. Machines are regarded as another source of variations with an assumption that the expectation of the quality characteristics produced by each machine is normally distributed. This study report the derivations of the computation of an effect of false alarm, and/or in different way the computation of...
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