Process monitoring tools control limits

The philosophy of SPC is to monitor the output of a process and determine when control action is necessary to correct deviations of the output from its setpoint. The most common tool for accomplishing this is the Shewhart (x-bar) chart shown in Figure 5.19. In the discrete parts manufacturing industries, multiple samples are taken at fixed intervals. Quality tests are run on these samples, and the mean is plotted on one Shewhart chart, and the range on another. In the absence of a disturbance, the means should be normally distributed around the setpoint. If the upper and lower control limits (UCL and LCL, respectively) are placed at three standard deviations above and below the target, a range is defined into which all of the means should fall. The... 

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. 

Control charts are an excellent analysis tool to both monitor and improve in-process performance during process development and later during production, where it is desired to follow process characteristics over time within batches or runs. The most common examples of tablet process characteristics that are measured in-process are weight, thickness, and hardness. The parameters measured need to be controllable so that adjustments can be made. During the initial runs, it is desirable to limit process adjustments to a minimum to observe the process in its natural state. Any adjustments made should be recorded and explained. Out-oflimit results should never be removed prior to performing a process capability analysis. If special cause variation is detected, then process improvements should be made to eliminate the special cause variation. 

In the era of single-loop control systems in chemical processing plants, there was little infrastructure for monitoring multivariable processes by using multivariate statistical techniques. A limited number of process and quality variables were measured in most plants, and use of univariate SPM tools for monitoring critical process and quality variables seemed appropriate. The installation of computerized data acquisition and storage systems, the availability of inexpensive sensors for typical process variables such as temperature, flow rate, and pressure, and the development of advanced chemical analysis systems that can provide reliable information on quality variables at high frequencies increased the number of variables measured at... 

The field of solid-phase synthesis instrumentation is continually advancing. Improvements in synthesis reagents, reaction monitoring, and instrument hardware and software will extend the limits of the instrumentation. As synthesizer capabilities improve, there is the potential that more and more control will be taken from the user until the instrument becomes a black box. It is important, however, to maintain an understanding of the principles of instrument operation and the chemistry that is being performed. The instrument is secondary to the chemistry but is an essential tool to help carry out the synthesis efficiently. The best instrument cannot improve ineffective chemistry and, conversely, a poorly designed instrument can compromise a very efficient chemical process. As long as the basic principles of reaction kinetics, fluid mechanics, and instrument safety are sustained, a solid-phase synthesizer can be used to its maximum potential and benefits. 

Normality assumption Most traditional SPC tools are based on the assumption that the process output characteristic is normally distributed, among which Shewhart control charts and multivariate control charts. In some cases, the central limit theorem can be used to justify approximate normality when monitoring means, but in numerous cases normality is an untenable assumption, and one is unwilling to use another parametric model. A number of nonparametric methods are available in these cases. As data availability increases, nonparametric methods seem especially useful in multivariate applications where most methods proposed thus far rely on normality. 

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