Excel statistical process control

The alternative is hexane, which because of the explosion hazard requires a more expensive type of extractor construction. After the extraction the product is dull gray. The continuos sheet is slit to the final width according to customer requirements, searched by fully automatic detectors for any pinholes, wound into rolls of about 1 m diameter (corresponding to a length of 900-1000 m), and packed for shipping. Such a continuous production process is excellently suited for supervision by modern quality assurance systems, such as statistical process control (SPC). Figures 7-9 give a schematic picture of the production process for microporous polyethylene separators. 

The intent in this chapter is not to present in great detail the mathematics behind the statistical methods discussed. An excellent reference manual assembled by the Automotive Industry Action Group (AIAG), Fundamental Statistical Process Control, details process control systems, variation, action on special or common causes, process control and capability, process improvement, control charting, and benefits derived from using each of these tools. Reprinted with permission from the Fundamental Statistacal Process Control Reference Manual (Chrysler, Ford, General Motors Supplier uality Requirements Task Force , Measurement Systems Analysis, MSA Second Edition, 1995, ASQC Press. 

The instrument has been evaluated by Luster, Whitman, and Fauth (Ref 20). They selected atomized Al, AP and NGu as materials for study that would be representative of proplnt ingredients. They found that only 2000 particles could be counted in 2 hours, a time arbitrarily chosen as feasible for control work. This number is not considered sufficient, as 18,000 particles are required for a 95% confidence level. Statistical analysis of results obtained for AP was impossible because of discrepancies In the data resulting from crystal growth and particle agglomeration. The sample of NGu could not be handled by the instrument because it consisted of a mixt of needles and chunky particles. They concluded that for dimensionally stable materials such as Al or carborundum, excellent agreement was found with other methods such as the Micromerograph or visual microscopic count. But because of the properties peculiar to AP and NGu, the Flying Spot Particle Resolver was not believed suitable for process control of these materials... 

Recent years have also witnessed exciting developments in the active control of unimolecular reactions [30,31]. Reactants can be prepared and their evolution interfered with on very short time scales, and coherent hght sources can be used to imprint information on molecular systems so as to produce more or less of specified products. Because a well-controlled unimolecular reaction is highly nonstatistical and presents an excellent example in which any statistical theory of the reaction dynamics would terribly fail, it is instmctive to comment on how to view the vast control possibihties, on the one hand, and various statistical theories of reaction rate, on the other hand. Note first that a controlled unimolecular reaction, most often subject to one or more external fields and manipulated within a very short time scale, undergoes nonequilibrium processes and is therefore not expected to be describable by any unimolecular reaction rate theory that assumes the existence of an equilibrium distribution of the internal energy of the molecule. Second, strong deviations Ifom statistical behavior in an uncontrolled unimolecular reaction can imply the existence of order in chaos and thus more possibilities for inexpensive active control of product formation. Third, most control scenarios rely on quantum interference effects that are neglected in classical reaction rate theory. Clearly, then, studies of controlled reaction dynamics and studies of statistical reaction rate theory complement each other. 

Physical models represent the real system by another physical system, in which jobs or customers move from one machine or service center to another and the machines or service centers perform processing operations on the jobs or customers. The major difference to the real system is that the model uses a different dimensional scale, so a large system will occnpy a table top. Physical models can use toy-sized components, but they can be provided with a control system that employs the same logic as the real system. Physical models are excellent as a means of edncating management and workers about the control of the system, but they do not lend themselves for assessing the long-run behavior of the system, as it is difficult to represent the statistical properties of events such as machine failures or worker absenteeism. 

The UK safety record of commercially prepared foods is excellent yet very occasionally isolated outbreaks of botulism associated with commercial products do occur. If we are to maintain a low incidence of botulism in this country it is essential that industry maintains careful control of processes, that the Government continues to accept the need for use of chemical preservatives (such as nitrite) in certain food products, and that the consumer refrains from home preservation of non-acid foods, other than by freezing. It is worthy of consideration whether, if nitrite were to be prohibited as a food preservative, some consumers might start home curing of pork in order to provide supplies of bacon and ham for domestic consumption. Such a situation could lead to a significant increase in risks for the consumer, and provide botulism statistics more akin to those of our EEC neighbours. 

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