Quality and Reliability Methods - SAS

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Contents Overview of Measurement Systems Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Example of Measurement Systems Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Launch the Measurement Systems Analysis Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126 Measurement Systems Analysis Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Average Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Range Chart or Standard Deviation Chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 EMP Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Effective Resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Shift Detection Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Bias Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Test-Retest Error Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Additional Example of Measurement Systems Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Statistical Details for Measurement Systems Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Chapter 8 Assess Measurement Systems 123 Overview of Measurement Systems Analysis Overview of Measurement Systems Analysis The EMP (Evaluating the Measurement Process) method in the Measurement Systems Analysis platform is largely based on the methods presented in Donald J. Wheeler’s book EMP III Using Imperfect Data (2006). The EMP method provides visual information and results that are easy to interpret and helps you improve your measurement system to its full potential. The Gauge R&R method analyzes how much of the variability is due to operator variation (reproducibility) and measurement variation (repeatability). Gauge R&R is available for many combinations of crossed and nested models, regardless of whether the model is balanced. For more information, see the “Variability Charts” chapter on page 151. Within the Six Sigma DMAIC methodology, MSA addresses the Measure phase and process behavior charts (or control charts) address the Control phase. MSA helps you predict and characterize future outcomes. You can use the information gleaned from MSA to help you interpret and configure your process behavior charts. For more information about Control Charts, see the “Interactive Control Charts” chapter on page 59. Example of Measurement Systems Analysis In this example, three operators measured the same five parts. See how the measurement system is performing, based on how much variation is found in the measurements. 1. Open the Gasket.jmp sample data table. 2. Select Analyze > Quality and Process > Measurement Systems Analysis. 3. Assign Y to the Y, Response role. 4. Assign Part to the Part, Sample ID role. 5. Assign Operator to the X, Grouping role. Notice that the MSA Method is set to EMP, the Chart Dispersion Option is set to Range, and the Model Type is set to Crossed. See Figure 8.5. 6. Click OK.
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Version 10 Quality and Reliability
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Nelson Rules . . . . . . . . . .
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8 8 Assess Measurement Systems The
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10 Other Menu Options . . . . . . .
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12 Reliability Forecast Platform Op
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Chapter 1 Learn about JMP Documenta
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Chapter 1 Learn about JMP 17 Book C
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Chapter 1 Learn about JMP 19 Book C
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Chapter 1 Learn about JMP 21 Additi
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Chapter 1 Learn about JMP 23 Additi
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Chapter 2 Statistical Control Chart
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Chapter 3 Introduction to Control C
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Chapter 4 Interactive Control Chart
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Chapter 5 Shewhart Control Charts V
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Chapter 10 Variability Charts 173 A
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Chapter 11 Pareto Plots The Pareto
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Chapter 11 Pareto Plots 181 Pareto
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Chapter 11 Pareto Plots 187 Launch
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Chapter 11 Pareto Plots 189 One-Way
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Chapter 11 Pareto Plots 191 Two-Way
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Chapter 11 Pareto Plots 193 Defect
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Chapter 11 Pareto Plots 195 Defect
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Chapter 12 Ishikawa Diagrams The Di
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Chapter 12 Ishikawa Diagrams 199 Pr
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Chapter 12 Ishikawa Diagrams 201 Bu
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Chapter 13 Lifetime Distribution Us
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Chapter 13 Lifetime Distribution 20
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Chapter 14 Lifetime Distribution II
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Chapter 15 Recurrence Analysis The
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Chapter 16 Degradation Using the De
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Chapter 16 Degradation 279 Overview
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Chapter 16 Degradation 281 The Degr
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Chapter 16 Degradation 283 Model Sp
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Chapter 16 Degradation 291 Inverse
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Chapter 16 Degradation 293 Platform
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Chapter 16 Degradation 295 Platform
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Chapter 16 Degradation 297 Model Re
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Chapter 16 Degradation 299 Destruct
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Chapter 16 Degradation 301 Stabilit
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Chapter 17 Forecasting Product Reli
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Chapter 18 Reliability Growth Using
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Chapter 18 Reliability Growth 319 O
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Chapter 18 Reliability Growth 321 E
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Chapter 18 Reliability Growth 327 T
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Chapter 18 Reliability Growth 329 F
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Chapter 18 Reliability Growth 343 A
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Chapter 18 Reliability Growth 345 A
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Chapter 18 Reliability Growth 347 S
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Chapter 19 Reliability and Survival
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References Abernethy, Robert B. (19
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References 407 Carroll, R.J., Ruppe
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References 409 Haaland, P.D. (1989)
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References 411 Lawless, J.F. (1982)
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References 413 Nelson, W.B. (2003),
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References 415 Snee, R.D. and Marqu
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Index JMP Quality and Reliability M
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Index 419 Fit SEV 242 Fit Weibull 2
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Index 421 Q Quantiles table 356 R R
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Index 423 Weibull Plot 360 Westgard
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Quality and Reliability Methods - SAS

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