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1 © The McGraw-Hill Companies, Inc., 2006 McGraw-Hill/Irwin Technical Note 9 Process Capability and Statistical Quality Control

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2 Basic Forms of Variation Assignable variation is caused by factors that can be clearly identified and possibly managed Common variation is inherent in the production process Example: A poorly trained employee that creates variation in finished product output. Example: A molding process that always leaves “burrs” or flaws on a molded item.

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3 Process Capability Process limits Specification limits How do the limits relate to one another?

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4 Types of Statistical Sampling Attribute (Go or no-go information) Defectives refers to the acceptability of product across a range of characteristics. Defects refers to the number of defects per unit which may be higher than the number of defectives. p-chart application Variable (Continuous) Usually measured by the mean and the standard deviation. X-bar and R chart applications

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5 Statistical Process Control (SPC) Charts UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 Normal Behavior Possible problem, investigate

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6 Control Limits are based on the Normal Curve x 0123-3-2 z Standard deviation units or “z” units.

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7 Control Limits We establish the Upper Control Limits (UCL) and the Lower Control Limits (LCL) with plus or minus 3 standard deviations from some x-bar or mean value. Based on this we can expect 99.7% of our sample observations to fall within these limits. x LCLUCL 99.7%

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8 Example of x-bar and R Charts: Required Data

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9 Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.

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10 Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values

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11 Example of x-bar and R charts: Steps 3&4. Calculate x- bar Chart and Plot Values UCL LCL

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12 Is the process in control Out of control if: 1.One point above or below the control limits 2.Two plots close to the upper or lower control limits 3.A run of 5 points above or below the central line 4.A trend of 5 points ascending or descending 5.Erratic behavior

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13 Example of x-bar and R charts: Steps 5&6. Calculate R- chart and Plot Values UCL LCL

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14 Example of Constructing a p-Chart: Required Data Sample No. No. of Samples Number of defects found in each sample

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15 Statistical Process Control Formulas: Attribute Measurements (p-Chart) Given: Compute control limits:

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16 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample Example of Constructing a p-chart: Step 1

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17 2. Calculate the average of the sample proportions 3. Calculate the standard deviation of the sample proportion Example of Constructing a p-chart: Steps 2&3

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18 4. Calculate the control limits UCL = 0.0924 LCL = -0.0204 (or 0) UCL = 0.0924 LCL = -0.0204 (or 0) Example of Constructing a p-chart: Step 4

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19 Example of Constructing a p-Chart: Step 5 UCL LCL 5. Plot the individual sample proportions, the average of the proportions, and the control limits 5. Plot the individual sample proportions, the average of the proportions, and the control limits

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Chapter 9A Process Capability and Statistical Quality Control

Chapter 9A Process Capability and Statistical Quality Control

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