Six-Sigma and Reliability

Dave Stewardson - ISRU with

Froydis Berke - Matforsk Norway

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

Presenters:

Introduction

Dave Stewardson - ISRU

and

Froydis Berke Matforsk

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

Soren Bisgaard

Leading Industrial Statistician

Overview of Six-Sigma

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