Generally weighted moving average control chart for monitoring process variability

Most applications of the EWMA control chart for monitoring processes depend on detecting shifts in the process mean. The problem of detecting an increase in process variability, which can also strongly affect the quality of products, is perhaps more important. When a process moves from the pilot phase to the production phase, the mean may not shift but the variation will probably increase because new sources of variation are introduced, including new people and materials. A simulation is performed to evaluate the ARL to false alarm and to monitor the change in the process variability of the EWMA control chart and the GWMA control chart. An extensive comparison reveals that the GWMA control chart is more sensitive than the EWMA control chart in monitoring the variance of a process. The results of this study can be applied to monitor the process variability in automated industries.     

Pattern recognition for statistical process control charts

Control charts are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Patterns displayed on control charts can provide information about the process. This paper describes the development of a pattern recognition system designed to detect and analyse various patterns that can occur on statistical quality control charts. The system looks not only for simple patterns, such as trend, shift and stratification, but also for superimposed patterns, such as trend + shift. The effect of noise associated with individual patterns is also analysed. The benefits of the approach compared with the alternatives are discussed.Notation N _i ith value of the noise series - N _T noise tolerance - x _i ith data item from a number sequence - r _i seed for random number simulation - adjacent difference - standard deviation - mean of the data - A slope of a straight line - B constant - C constant - i indexing integer - j indexing integer - k total number of samples - l starting point of a pattern on control chart - m ending point of a pattern on control chart - n size of samples - ptn pointer to the pattern identified - slope slope for trend patterns - X normally distributed variate arising from simulation - CL centre-line - LCL lower control limit - LOSL lower one-sigma limit - LWL lower warning limit - UCL upper control limit - UOSL upper one-sigma limit - UWL upper warning limit     

Monitoring the coefficient of variation using a variable sample size control chart

This paper proposes an adaptive Shewhart control chart implementing a variable sample size strategy in order to monitor the coefficient of variation. The goals of this paper are as follows: (a) to propose an easy-to-use 3-parameter logarithmic transformation for the coefficient of variation in order to handle the variable sample size aspect; (b) to derive the formulas for computing the average run length, the standard deviation run length, and the average sample size and to evaluate the performance of the proposed chart based on these criteria; (c) to present ready-to-use tables with optimal chart parameters minimizing the out-of-control average run length as well as the out-of-control average sample size; and (d) to compare this chart with the fixed sampling rate, variable sampling interval, and synthetic control charts. An example illustrates the use of the variable sample size control chart on real data gathered from a casting process.     

Distribution-free phase-II exponentially weighted moving average schemes for joint monitoring of location and scale based on subgroup samples

In investment casting, the separation of cast components composed of strong, hard materials from the gating system is a delicate operation, which can incur considerable costs in terms of labor and tools. In this study, we substituted conventional grinder cutting with vibration-induced fatigue failure to facilitate the separation of chromium-molybdenum (CrMo) alloy steel components from an investment casting tree. This process involves the creation of V-shape notches on the ingate structure as well as modifications to component layout within the pattern tree. Vibration-excited dynamic experiments were performed to examine the effects of notch designs on stress concentration and cutting off at the ingates. Mold flow analysis was used to optimize the design of the pouring system to ensure casts of high quality. Finite element method (FEM) and experimental modal analysis (EMA) were conducted to predict the efficacy of the model and vibration characteristics. The application of harmonic response analysis to a casting tree model determined the maximum and minimum principal stresses at ingate notches. Once the stress values at the ingate notches were sufficient to ensure failure conditions, the same parameters were used to perform a final experiment for verification. Experiment results revealed that the breaks indeed occurred at the notches of the ingate. The proposed approach could be used as an alternative to conventional cutting using a grinding wheel as a means of reducing labor costs, increasing safety, and enhancing production efficiency.     

Optimal inspection policy for three-state systems monitored by variable sample size control charts

This paper presents the expected long-run cost per unit time for a system monitored by an adaptive control chart with variable sample sizes: if the control chart signals that the system is out of control, the sampling which follows will be conducted with a larger sample size. The system is supposed to have three states: in-control, out-of-control, and failed. Two levels of repair are applied to maintain the system. A minor repair will be conducted if an assignable cause is confirmed by an inspection, and a major repair will be performed if the system fails. Both the minor and major repairs are assumed to be perfect. We derive the expected long-run cost per unit time, which can be used to obtain the optimal inspection policy. Numerical examples are conducted to validate the derived cost.     

An enhancement of DSI X̄ control charts using a fuzzy-genetic approach

A traditional control chart used to monitor a process draws the process data at a fixed sampling rate, while a variable sampling interval (VSI) control chart varies the sampling rate as a function of on-line process data. In such a sampling policy, a higher sampling rate is adopted when there is suspicion of a change in a process. Therefore, it is able to detect the process change faster than traditional control chart, and thus has been much accepted for use. Nevertheless, the binary suspicious grade used in VSI policy to specify the sampling rate is not detailed enough to explain the acquired information from process data. As a result, this paper aims to refine the suspicious grade and sampling interval lengths to increase the detection ability of VSI charts. This study first establishes a composition function on two sides of the control chart by introducing the concept of fuzzily suspicious grade to specify the sampling rate. Then, genetic algorithms (GAs) is used to adjust the values of the parameters in this composition function to enhance the dual-sampling-interval (DSI) charts-one type of the VSI charts in common use-in terms of average time to signal (ATS) for process mean shift. In addition, some statistical properties of the enhanced DSI charts as well as performance comparison to traditional DSI charts are provided and analysed.     

A synthetic control chart for monitoring process dispersion with sample range

A synthetic control chart for monitoring the changes in the standard deviation of a normally distributed process is proposed in this paper. The synthetic chart consists of the sample range (R) chart and the conforming run-length (CRL) chart. The R chart can be viewed as a special case of the synthetic chart. The operation, design and performance of this chart are described. Average run- length comparisons between other procedures and the synthetic chart are presented. It indicates that the synthetic chart is a good alternative for monitoring process dispersion. The variable sampling interval (VSI) schemes, as an enhancement to the synthetic chart, are discussed to further improve the chart performance. An example is presented to illustrate the application of synthetic chart and its VSI scheme.     

A new adaptive control chart for monitoring process mean and variability

Traditionally, an $\bar{X}$ chart is used to control the process mean, and an R chart is used to control the variance. However, these charts are not sensitive to the small shifts in the processes. The adaptive charts might be considered if the aim is to detect process changes quickly. In this paper, we propose a new adaptive single control chart which integrates the exponentially weighted moving average procedure with the generalized likelihood ratio test statistics for jointly monitoring both the process mean and variability. This new chart is effective in detecting the disturbances that shift the process mean, increase or decrease the process variance, or lead to a combination of both effects.     

Monitoring of the concrete curing process using plastic optical fibers

In this paper we propose the application of plastic optical fiber to monitor the concrete curing process.The proposed method is based in the scattering of the propagated optical signal in grooves imposed to the fiber. By monitoring the intensity of the transmitted light signal, along time, we can determine the cement setting rate along all the curing period. The obtained results show that the system has enough sensitivity to analyze a curing period of 28 days, where the received optical power is 5% of the initial value.     

On-line process monitoring during transient operations using weighted distance Auto Associative Bilateral Kernel Regression

In this paper, a new data-driven auto associative bilateral kernel regression (AABKR) method based on weighted distance is proposed for the on-line monitoring of transient process operations. A bilateral approach to the kernel regression formulates a representative model that uses both the spatial and temporal information in the data, and a new weighted-distance algorithm captures temporal information. Moreover, an adaptive approach is proposed to dynamically compensate for faulty process inputs in the bilateral kernel evaluations, providing a robust model with little spillover. The proposed weighted-distance AABKR is first implemented using numerical process examples and then applied to the transient start-up operation of a nuclear power plant. Monte Carlo simulation results are provided by randomly assigning fault sensors and fault magnitudes. The results demonstrate the feasibility and efficiency of the proposed method.     

Monitoring of fat crystallization process using UVP–PD technique

The dynamic response of the cocoa butter shear crystallization process to a step reduction in temperature of a two stage shear crystallizer is investigated by measuring the pulsed ultrasound Doppler based velocity profile (UVP) and pressure drop (PD) in a pipe section. In addition, the velocity of sound, attenuated amplitude of the transmitted signal and temperature are continuously recorded. The temporal variation in rheological properties such as the apparent viscosity at different shear rates and the corresponding radial position in the pipe are determined by fitting the velocity profile and pressure drop to the power law rheological model. The linear dependence of sound velocity on the solid fat content (SFC) in the cocoa butter crystal suspension previously determined using the nuclear magnetic resonance technique is used to characterize crystallization. The cocoa butter crystal suspension is found to be shear thinning, the value of the power law exponent decreasing with increase in SFC. Newly developed software is used to integrate on-line measurement of flow profiles, pressure difference, temperature, velocity of sound and the attenuated amplitude of the transmitted signal. The software also calculates velocity profiles using spectral signal analysis, determines the rheological properties, and provides a graphical user interface and tools for data visualization. It is demonstrated that the cocoa butter shear crystallization process can be monitored using the UVP–PD technique.     

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes.     

Statistical process control with intelligence using fuzzy ART neural networks

With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.     

Statistical process control with intelligence using fuzzy ART neural networks

With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.     

Nonlinear predictive control of a drying process using genetic algorithms

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control.     

Construct MIMO process control system by using soft computing methods

The main objective of this study aims at multiple-input multiple-output (MIMO) process mode. Based on the integrated concepts of statistical process control (SPC) and engineering process control (EPC), soft computing (SC) technique and statistical analysis technique are combined to modularize the relationship between process output and process input, so optimal yield can be derived and process quality can be improved. This study intended to construct a MIMO process control system with soft computing methods for prediction and parameter control and detailed the internal operation for each sub-system and relationship among one another. Besides correct prediction and diagnosis for the noise due to system deviation, it effectively controls process input and output as well as achieves process optimization.