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ClassNotes03 - Statistical Quality Control OEM 2009 ESI...

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Statistical Quality Control OEM 2009 ESI 6321 –Applied Probability Methods in Engineering 2 Statistical Quality Control A collection of tools used to monitor and guide quality improvement activities. Quality of design: Quality level (performance, reliability, function) that is the result of engineering and/or management decisions Quality of conformance: Reduction of variability Elimination of defects Goal: all units are identical, and defect-free
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3 Process Variability Any production process contains variability. Chance (common) causes of variability Essentially unavoidable, inherent variability in the process Assignable (special) causes of variability Avoidable variability in the process: Improperly adjusted machines Operator errors Defective raw materials 4 Control Chart Process is in control: Only chance causes of variability are present Process is out of control: Assignable causes of variability are present The most powerful of all Statistical Quality Control tools is the control chart . A control chart displays periodic measurements of some quality characteristic, and visualizes whether the process is in or out of control.
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5 Control Chart A control chart consists of a center line and two control limits for the value of the quality characteristic that we are interested in. If the observed value of the quality characteristic falls beyond one of the two control limits, the process is out of control . This type of control chart is called a Shewhart control chart . 6 Variables vs. Attributes Control Chart There are two general types of control charts: Variables control charts. Attributes control charts. Variable control charts are used for quality characteristics that can be measured on a continuous scale. Attribute control charts are used for discrete quality characteristics; for instance: conforming vs. nonconforming.
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7 Control Chart for Mean The most widely used variables control chart monitors the mean of a process parameter. This mean is then the quality characteristic of interest. It is often reasonable to assume that the quality characteristic is normally distributed (when the process is in control). We will base our choice of center line and control limits on this assumption. In principle, the approach can be generalized to other population distributions. 8 Designing a Control Chart Example: consider the manufacturing of automobile piston rings. A critical quality characteristic is the average realized inside ring diameter. We know that the process mean inside ring diameter is 74 mm, and its standard deviation is 0.01 mm if the process is in control . Every hour a random sample of 5 rings is taken, and their average inside ring diameter is computed and plotted in a control chart.
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