Download presentation

Presentation is loading. Please wait.

Published bySeth Lloyd Modified over 4 years ago

1
2.008 -Spring 20041 2.008 Process Control

2
2.008 -Spring 20042 Outline 1.Optimization 2.Statistical Process Control 3.In-Process Control

3
2.008 -Spring 20043 What is quality?

4
2.008 -Spring 20044 Variation: Common and Special Causes Pieces vary from each other: But they form a pattern that, if stable, is called a distribution: Size

5
2.008 -Spring 20045 Common and Special Causes (contd) Distributions can differ in… …or any combination of these Size LocationSpreadShape

6
2.008 -Spring 20046 Common and Special Causes (contd) If only common causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable: Size Prediction Time

7
2.008 -Spring 20047 Common and Special Causes (contd) IF special causes of variation are present, the process output is not stable over time and is not predictable: Size Prediction Time

8
2.008 -Spring 20048 Variation: Run Chart Specified diameter = 0.490 +/-0.001 Diameter of part Run number Process change 0.4915 0.4910 0.4905 0.4900 0.4895 0.4890 0.4885 0.4880 01020304050

9
2.008 -Spring 20049 Process Control In Control (Special causes eliminated) Out of Control (Special causes present) Time Size

10
2.008 -Spring 200410 Statistical Process Control 1.Detect disturbances (special causes) 2.Take corrective actions

11
2.008 -Spring 200411 Central Limit Theorem A large number of independent events have a continuous probability density function that is normal in shape. Averaging more samples increases the precision of the estimate of the average.

12
2.008 -Spring 200412 Shewhart Control Chart Average (of 10 samples) Diameter Run number Upper control limit Lower control limit 1.010 0.990 0.995 1.000 1.005 0102030405060708090100

13
2.008 -Spring 200413 Sampling and Histogram Creation Wheel of Fortune: Equal probability of outcome 1-10, P=0.1 Taking 100 random samples, the resulting histogram would look like this Taking random samples, the resulting histogram would look like this 1 2 3 4 5 6 7 8 9 10 Frequency 20 18 16 14 12 10 8 6 4 2 0 Outcome 1 2 3 4 5 6 7 8 9 10 Frequency Outcome

14
2.008 -Spring 200414 Sampling and Histogram Creation (contd) Wheel of Fortune: Equal probability of outcome 1-10, P=0.1 Take 10 random samples, calculate their average, and repeat 100 times, the resulting histogram would resemble Take 10 random samples, calculate their average, and repeat times, the resulting histogram would approach the continuous distribution shown 1 2 3 4 5 6 7 8 9 10 Frequency 20 18 16 14 12 10 8 6 4 2 0 Outcome 1 2 3 4 5 6 7 8 9 10

15
2.008 -Spring 200415 Uniform Distributions Underlying Distribution Probability distribution 1.5 1 0.5 0 0 0.5 1 1.5 1 0.5 0 0 0.5 1 Probability distribution Distribution resulting from averaging of 2 random values Distribution resulting from averaging of 3 random values 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Distribution resulting from averaging of 10 random values 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

16
2.008 -Spring 200416 Normal Distribution Distribution resulting from averaging of 3 random values 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Distribution resulting from averaging of 10 random values 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Underlying Distribution 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Distribution resulting from averaging of 2 random values 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

17
2.008 -Spring 200417 Precision Not precisePrecise Not accurate Accurate

18
2.008 -Spring 200418 Shewhart Control Chart Upper Control Limit (UCL), Lower Control Limit (LCL) Subgroup size ( 5 < n < 20 )

19
2.008 -Spring 200419 Shewhart Control Chart (contd) Control chart based on 100 samples Average (of 10 samples) Diameter Run number Upper control limit Lower control limit 1.010 0.990 0.995 1.000 1.005 0102030405060708090100 Step disturbance

20
2.008 -Spring 200420 Shewhart Control Chart (contd) Average (of 10 samples) Diameter Run number Upper control limit Lower control limit 1.010 0.990 0.995 1.000 1.005 0102030405060708090100 Step disturbance Control chart based on average of 10 samples, note the step change that occurs at run 51

21
2.008 -Spring 200421 Control Charts 1.Collection: Gather data and plot on chart. 2.Control Calculate control limits from process data, using simple formulae. Identify special causes of variation; take local actions to correct. 3.Capability Quantify common cause variation; take action on the system. These three phases are repeated for continuing process improvement. Upper control limit Process average Lower control limit 0102030405060708090100 Run number

22
2.008 -Spring 200422 Setting the Limits Idea: Points outside the limits will signal that something is wrong-an assignable cause. We want limits set so that assignable causes are highlighted, but few random causes are highlighted accidentally. Convention for Control Charts: Upper control limit (UCL) = x + 3σ sg Lower control limit (LCL) = x-3σ sg (Where σ sg represents the standard deviation of a subgroup of samples)

23
2.008 -Spring 200423 Setting the Limits (contd) Convention for Control Charts (contd): As n increases, the UCL and LCL move closer to the center line, making the control chart more sensitive to shifts in the mean.

24
2.008 -Spring 200424 Benefits of Control Charts Properly used, control charts can: Be used by operators for ongoing control of a process Help the process perform consistently, predictably, for quality and cost Allow the process to achieve: –Higher quality –Lower unit cost –Higher effective capacity Provide a common language for discussing process performance Distinguish special from common causes of variation; as a guide to local or management action

25
2.008 -Spring 200425 Process Capability Lower specification limit Upper specification limit In Control and Capable (Variation from common cause reduced) In Control but not Capable (Variation from common cause excessive) Size Time

26
2.008 -Spring 200426 Process Capability Index 1.Cp = Range/6 2.Cpk

27
2.008 -Spring 200427 Process Capability Take an example with: Mean =.738 Standard deviation, σ =.0725 UCL = 0.900 LCL = 0.500 Normalizing the specifications:

28
2.008 -Spring 200428 Process Capability (contd) Using the tables of areas under the Normal Curve, the proportions out of specification would be: P UCL = 0.0129 P LCL = 0.0005 P total = 0.0134 The Capability Index would be: C PK = Z min /3 = 2.23 / 3 = 0.74

29
2.008 -Spring 200429 Process Capability (contd) If this process could be adjusted toward the center of the specification, the proportion of parts falling beyond either or both specification limits might be reduced, even with no change in Standard deviation. For example, if we confirmed with control charts a new mean = 0.700, then:

30
2.008 -Spring 200430 Process Capability (contd) The proportions out of specification would be: P UCL = 0.0029 P LCL = 0.0029 P total = 0.0058 The Capability Index would be: C PK = Z min /3 = 2.76 / 3 = 0.92

31
2.008 -Spring 200431 Improving Process Capability To improve the chronic performance of the process, concentrate on the common causes that affect all periods. These will usually require management action on the system to correct. Chart and analyze the revised process: –Confirm the effectiveness of the system by continued monitoring of the Control Chart

Similar presentations

OK

A Tailorable Environment for Assessing the Quality of Deployment Architectures in Highly Distributed Settings Sam Malek and Marija Mikic-Rakic Nels Beckman.

A Tailorable Environment for Assessing the Quality of Deployment Architectures in Highly Distributed Settings Sam Malek and Marija Mikic-Rakic Nels Beckman.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google