Statistical measurement control

The recent proliferation of fast and convenient instruments for particle size analysis has brought with it, for the first time in this subject, a practical 

A measurement process is said to be under statistical control when all the critical parameters and conditions are under sufficient control such that any variation in data from repeated measurements does not change over an extended period of time. The variation (scatter) must also be demonstrated to occur in a random fashion. The best way of checking this is to use control charts in which the mean values and standard deviations of replicate measurements are plotted against time and the data are checked to be within statistically derived limits. 

The replicate measurements (at least two each time) are made over a prolonged period of time (at least 40 times), on a check-standard sample which must remain constant and homogeneous during the measurement cycle. Software packages are available to assist in the evaluation of results and in plotting the control charts, and any measurements outside the control limits are automatically highlighted. Visual inspection of the charts may reveal systematic trends on drifts. The variability is then analysed, its causes identified and brought under control and reduced. 

Apart from data on precision (repeatability, reproducibility), control charts may be used to check or monitor accuracy, i.e. to what extent the measured value of a quantity (such as the mean particle size, for example) agrees with the accepted value for that quantity, bearing in mind that the true value is never known. In testing accuracy, the standard test powders available on the market may be used. 

It is beyond the scope of this book to give more than a quick review of this important subject. The interest in and application of the statistical-measurement control techniques in particle size measurement, however, are certain to continue to grow in the future. 

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Statistical Process Control. A properly miming production process is characterized by the random variation of the process parameters for a series of lots or measurements. The SPG approach is a statistical technique used to monitor variation in a process. If the variation is not random, action is taken to locate and eliminate the cause of the lack of randomness, returning the process or measurement to a state of statistical control, ie, of exhibiting only random variation. 

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

Statistical quaUty control charts of variables are plots of measurement data, preferably the average result of repHcate analyses, vs time (Fig. 2). Time is often represented by the sequence of batches or analyses. The average of all the data points and the upper and lower control limits are drawn on the chart. The control limits are closely approximated by the sum of the grand average plus for the upper control limit, or minus for the lower control limit, three times the standard deviation. 

Statistical quality control is used to first measure and then continuously improve product quality. For example, The Dow Chemical Company s average 1989 performance compared to the typical sales specification were purity, = 99.65 wt % color, APHA = 4 acid (as HCl) = 7.3 ppm and water = 26 ppm. Averages of properties were based on rail car and tank tmck shipment samples during 1989. 

Fig. 4.12. Statistical process control chart for TXRF measurement systems. The sensitivity of the system can be controlled by daily calibration with an...

Failure modes analysis Statistical process control Measurement systems analysis Employee motivation On-the-job training Efficiency will increase through common application of requirements for Continuous improvement in cost Continuous improvement in productivity Employee motivation On-the-job training... 

You should review the contract and the detail specifications to identify whether your existing controls will regulate quality within the limits required. You may need to change the limits, the standards, the techniques, the methods, the environment, and the instruments used to measure quality characteristics. One technique may be to introduce Just-in-time as a means of overcoming storage problems and eliminating receipt inspection. Another technique may be Statistical Process Control as a means of increasing the process yield. The introduction of these techniques needs to be planned and carefully implemented. 

You will need to produce more than one part to verify that the process is stable. You need to form a sample large enough to take statistical measurement. If the measurements taken on the product fall within the central third of the control limits then the set-up can be approved - if not, then adjustments should be made and further samples produced until this condition is achieved. The Note in clause 4.9.4 indicates that regardless of the number of parts in the sample, it is the comparisons made on the last part that establish the conditions for commencement of production. 

Metrics for this might include number of excursions from statistical process control, but one very useful metric for controllability is process capability, or more accurately, process capability indices. Process capability compares the output of an in-control process to the specification limits by using capability indices. The comparison is made by forming the ratio of the spread between the process specifications (the specification width ) to the spread of the process values. In a six-sigma environment, this is measured by six standard deviation units for the process (the process width ). A process under control is one where almost all the measurements fall inside the specification limits. The general formula for process capability index is ... 

Statistical process control charts (such as the x-bar and range charts) plot measurements as a function of time [Grant and Leavenworth (1988)]. With reference to the current day, what part of these charts approximates an enumerative study What part of these charts approximates an analytic study Are the parts different Are the uses different ... 

Figure 10.2 Statistical process control charts for clearings. Top panel runs chart showing clearings as a function of measurement number. Middle panel x-bar chart with dashed upper control limit (UCL) and lower control limit (LCL) solid horizontal line is the grand mean, X. Bottom panel range chart with dashed upper control limit (UCL) solid horizontal line is the average range, r.

Wheeler, D.J., and Lyday, R.W. (1984), Evaluating the Measurement Process, Statistical Process Controls, Knoxville, TN. 

In addition to a tight distribution of the thickness variation within a wafer, the average of a group of individual thicknesses must also be targeted within a certain range. Statistically, the control of the WIWNU is the control of standard deviation of individual thicknesses, and the control of final thickness post CMP is the control of the mean. The variation of the mean from wafer to wafer is called wafer-to-wafer nonuniformity (WTWNU). All the thicknesses mentioned in this section are actually the means of many individual thickness measurements in each wafer. Control is not easy, for reasons discussed in the following. 

Unlike SPC techniques, standard feedback control methods such as PID-control, do exert control upon a process, in an effort to minimize y, — yk. Control in Statistical Process Control is as such not regulatory control, but a semantic means of relating SPC to quality control—a means that often leads to the hybrid term SQC. Ogunnaike and Ray [14, Sec. 28.4] offer advice on when to use SPC and when to use standard feedback control methods When the sampling interval is much greater than the process response time, when zero-mean Gaussian measurement noise dominates process disturbances, and when the cost of regulatory control action is considerable, SPC is preferred. 

The PAT guidance facilitates introduction of new measurement and control tools in conjunction with well-established statistical methods such as design of experiments and statistical process control. It, therefore, can provide more effective means for product and process design and control, alternate efficient approaches for quality assurance, and a means for moving away from the corrective action to a continuous improvement paradigm. 

Each manufacturer of a packaging component sold to a drug product manufacturer should provide a description of the quality control measures used to maintain consistency in the physical and chemical characteristics of the component. These measures generally include release criteria (and test methods, if appropriate) and a description of the manufacturing procedure. If the release of the packaging component is based on statistical process control, a complete description of the process (including control criteria) and its validation should be provided. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

The licensee shall establish and maintain a statistical control system including control charts and formal statistical procedures, designed to monitor the quality of each type of program measurement. Control chart limits shall be established to be equivalent to levels of (statistical) significance of 0.05 and 0.001. Whenever control data exceed the 0.05 control limits, the licensee shall investigate the condition and take corrective action in a timely manner. 

To establish reasonable acceptance criteria for accuracy during planning, we should obtain statistical laboratory control limits from the laboratory that will perform analysis for the project samples. We should also be aware of matrix interferences in environmental samples that may reduce the accuracy of analysis. As part of QC procedures, to estimate the effects of matrix interference on accuracy, laboratories perform the accuracy determinations on environmental samples, known as matrix spike (MS) and matrix spike duplicate (MSD). These fortified samples enable the laboratory to detect the presence of interferences in the analyzed matrices and to estimate their effect on the accuracy of sample analysis. (In the absence of matrix interferences, an additional benefit from MS/MSD analysis is an extra measure of analytical precision calculated as the RPD between the two recoveries.)... 

In industrial plants, large numbers of process variables must be maintained within specified limits in order for the plant to operate properly. Excursions of key variables beyond these limits can have significant consequences for plant safety, the environment, product quality and plant profitability. Statistical process control (SPC), also called statistical quality control (SQC), involves the application of statistical techniques to determine whether a process is operating normally or abnormally. Thus, SPC is a process monitoring technique that relies on quality control charts to monitor measured variables, especially product quality. 

Statistical process control methods are applied to preparative chromatography for the case where cut points for the effluent fractions are determined by on-line species-specific detection (e.g., analytical chromatography). A simple, practical method is developed to maximize the yield of a desired component while maintaining a required level of product purity in the presence of measurement error and external disturbances. Relations are developed for determining tuning parameters such as the regulatory system gain. 

The Shotscope system also maintains and displays statistical process control (SPC) data in a variety of formats, including trend charts, X-bar and R charts, histograms, and scatter diagrams. This information provides molders with the knowledge that their processes are in control, and, should they go out of control, Shotscope can alert to an out-of-control condition and divert suspect-quality parts. Furthermore, because the Shotscope system can measure and archive up to 50 process parameters (such as pressures, temperatures, times, etc.) for every shot monitored and the information archived, the processing fingerprint for any part can be stored and retrieved at any time in the future. This functionality is extremely important to any manufacturer concerned with the potential failure of a molded part in its end-use application (for example, medical devices). 

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