six-sigma and reliability dave stewardson - isru froydis berke - matforsk soren bisgaard - usa poul...
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Six-Sigma and Reliability
Dave Stewardson - ISRU
Froydis Berke - Matforsk
Soren Bisgaard - USA
Poul Thyregod - Denmark
Bo Bergman - Sweden
Pro-Enbis
All joint authors - presenters- are members of:
Pro-Enbis and ENBIS.
This presentation is supported by Pro-Enbis a Thematic Network funded under the ‘Growth’ programme of the European Commission’s 5th Framework research programme - contract number G6RT-CT-2001-05059
Introduction
Dave Stewardson - ISRU
Rational for Six-Sigma
Improve processes
Team - project based improvement
Properly costed benefits
Grow your own expertise
Visible success
Use of modern improvement tools
Rational for Modern Maintenance
Preventative maintenance
Condition monitoring
Better planning
Less machine downtime
Operators monitor machine and process condition
Rationales Fit!
Everyone involved
Monitoring to help operators get better control over the process
Publicise success
Maintenance and Reliability
We can use six-sigma to crack maintenance problems
Strategy is the same
What is ‘reliability’ ?
Synopsis of ReliabilitySome Definitions
1) “The probability that the product continues to meet the specification”.2) “The probability that an item will perform as required, under stated conditions, for a stated period of time”.3) “The mean lifetime of a product”.4) “The likelihood that a product will survive stated stresses”.5) “The survival rate of something”.6) “Resistance to failure”.7) “How long we expect a thing to last”.
Related to:
•Quality•Survival•Product Guarantees•Product Improvement•Process Control•Process Capability•Failure Modes Analysis•Problem Solving•Statistical Modelling•Quality Engineering•Preventative maintenance
Relationship of Weibull to Statistics and modelling generally
IndustrialStatistics
StatisticalModelling
Reliability
WeibullDistribution
Web-page example from Quality Digest
By Thomas Pyzdek a consultant in Six Sigma.
http://www.qualitydigest.com/june01/html/sixsigma.html
Web-Page Example II
•Project was initiated by a group of senior leaders, •After receiving numerous customer complaints. •Pareto analysis on customer issues raised in the previous 12 months. •Solder problems were the No. 1 problem for customers.
Web-Page Example•A program manager chosen•Six Sigma team was formed •A Master Black Belt provided technical leadership. •The team began working through the design, measure, analyze, improve and control cycle. •Defined critical-to-quality measures, • Pareto analysis applied to the types of solder defects. •A wave solder team was formed included a process engineer, machine operator, an inspector and a touch-up solder operator.
Web-Page Example
•A Black Belt providing training
• The team identified and assigned various tasks, • data collection, •creating "as is" and "should be" process maps•Performed process audits.
Web-Page Example
Discovered:•‘Touch-up’ was performed before any data were collected. •Because solder problems were routine, touch-up was considered part of the soldering process.•There were 24 full-time personnel and four full-time inspectors assigned to touch-up.•Most of the defects were touch-up defects, not wave solder defects.•The equipment desperately needed maintenance. •No preventive maintenance program was in place.
Web-Page Example
Recommended several immediate changes:
1. Conduct inspection immediately after wave solder and before touch up. (Process Change! djs)
2. Use a control chart to analyze the results.
3. Perform a complete maintenance of the process.
Web-Page Example
Defects dropped by 50 percent within a month
Began DOEs
•DOEs revealed that the majority of prior assumptions were false•sometimes the results were precisely the opposite of the accepted point of view.•Significant quality and cost savings resulted as the new knowledge was used to modify procedures.
Web-Page Example.
Eventually defect rate in the area dropped by 1,000 percent over a period of 10 months.
Productivity increased by 500 percent in terms of labor hours per board.
DoE and Reliability
Example
From:
Using Designed Experiments and the analysis of Statistical Error to determine Change Points in
Fatigue Crack Growth Rates.
1University of Newcastle, 2Corus Group UK, 3Instituto de Engenharia Mecanica e Gestao Industrial, Porto, Portugal, 4Centro Sviluppo Materiali, Italy,5Voest-Alpine, Austria,6Thyssen Krupp, Germany, 7Sogerail, France
Main Objective
Determine the effects of stress ratio and relative humidity on the fatigue crack growth rates measured in grade 260 rail steel - Reliability Approximately 75% of the rails currently produced for use in Europe are 260 grade.
Started just before Hatfield crash!
The Reliability Test
Rail samples subjected to variable stress levels under a constant cycle
Crack introduced into the sample
Growth of crack measured over time against number of cracks
Analysis of da/dN verses the stress intensity
Experimental Design
Two stages, first considered a screening stage involving 2 Labs only.
Design constrained by limit on material resource.
Biggest problem - how to interpret the data?
Design Factor Settings
Factorial Points
Test Number Rail Manufacturer Laboratory Relative Humidity Stress Ratio
A1 1 B ~60% 0.5A2 1 A <=10% 0.5A3 1 A ~60% 0.2A4 1 B <=10% 0.2A6 2 A ~60% 0.5A7 2 B <=10% 0.5A8 2 B ~60% 0.2A9 2 A <=10% 0.2A11 3 A ~60% 0.5A12 3 B <=10% 0.5A13 3 B ~60% 0.2A14 3 A <=10% 0.2A16 4 B ~60% 0.5A17 4 A <=10% 0.5A18 4 A ~60% 0.2A19 4 B <=10% 0.2
Centre Points
A5 1 A ~35% 0.5A10 2 A ~35% 0.2A15 3 B ~35% 0.5A20 4 B ~35% 0.2
Crack length v Cycles
14.000
16.000
18.000
20.000
22.000
24.000
26.000
28.000
30.000
0 100000 200000 300000 400000 500000 600000 700000
Cycles, N
Fatigue crack growth rate
1.0000E-09
1.0000E-08
1.0000E-07
1.0000E-06
1.0000 10.0000 100.0000
Stress Intensity Factor Range, Delta K (MPa.m̂0.5)
Plot of S for 5ptMA(2)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 2 4 6 8 10 12 14 16 18
5ptMA of Slope(2)
1
3
5
79
11
13
15
0 2 4 6 8 10 12 14 16 18
Plan for second stage
1) Stress Ratio is important so fix it at a convenient value
2) Add Cyclic Frequency as a factor
3) Just monitor Relative Humidity and Temperature
Factor settings for Part 2 TestsTest
NumberRail
ManufacturerLaboratory Cyclic
Frequency: HzTemperature C Relative
Humidity, %B1 1 C 15 Record RecordB2 1 D 15 Record RecordB3 1 E 120 Record RecordB4 1 F 10 Record RecordB5 1 C 70 Record RecordB6 2 C 70 Record RecordB7 2 D 15 Record RecordB8 2 E 120 Record RecordB9 2 F 10 Record Record
B10 2 D 15 Record RecordB11 3 C 15 Record RecordB12 3 D 15 Record RecordB13 3 E 120 Record RecordB14 3 F 15 Record RecordB15 3 E 120 Record RecordB16 4 C 70 Record RecordB17 4 D 15 Record RecordB18 4 E 120 Record RecordB19 4 F 10 Record RecordB20 4 F 15 Record RecordA1 1 B 10 Record 60A2 1 A 15 Record < 10A5 1 A 15 Record 35A6 2 A 15 Record 60A7 2 B 10 Record < 10A11 3 A 15 Record 60A12 3 B 10 Record < 10A15 3 B 10 Record 35A16 4 B 10 Record 60A17 4 A 15 Record < 10
Project Findings
Found most important factors
Can now set these at optimum
Found a good way to use the data
Can monitor the quality of rails
Better understanding of factors effecting reliability of rails
Conclusions were
Experimental design helped to discover the important factors that effect these types of Reliability test.
It is also possible to derive quality monitoring of the test data using charts of the Plot parameters; slope, error and intercept.
Corus engineers now use these methods - training by ISRU
Six-Sigma and Maintenance
Condition Monitoring
Test Equipment Condition Monitoring
Ericcson (Sweden)
Routine testing of electric components
If test kit failed (equipment not working)
Could fail a good component
Conducted designed Experiment to optimise a monitoring scheme
Condition Monitoring II
Discovered potential problems with kit
Found an optimum scheme
Developed control charts
Discovered that the number of tests per day was not the major influence
The worse the product quality, the more likely the test kit would fail to work properly
Condition Monitoring III
Other examples:
1. Ohio – monitoring of large weighing equipment (50 Tonnes)
Effected by by weather – and animals
2. Monitoring of measuring equipment used for calibration – Electrolux
General Problems
Lack of good data
Spend time to collect this
But then USE IT
Must drive it on!
Must see benefits quickly!
Best Strategy
Involve the operators directly
makes it ‘easier’ for the engineers
Work as a team