seminar on statistical process control on cylinder liners

8/14/2019 seminar on statistical process control on cylinder liners

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ABSTRACTProcess Capability

Two concepts need to be considered:

Process capability is determined by the variations that

comes from common cause.

Customers, internal or external, are more typically

concerned with the overall output of the process.


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Statistical Quality Control Software

The software stores information in a databaseand develops the control chart for the data.

The data may be retrieved any time on userrequest.

The multiple document interface help in theview ability and accessibility of the data.

It works on set guidelines, the specificationcan be user defined.


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INTRODUCTIONMachining ProcessProcess control plan are made mainly on the following processsuch as,

Outer diameter turning (Rough) Inner diameter turning (Rough)Outer diameter turning (Finish)Finish bore turning

Rough honingCNC turningFinish honingPlato honing.


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OVERVIEW OF SQCStatistics :

Collection, tabulation, analysis and interpretation of the data.

Quality :Fitness for Use.

Control :

Directing and maintaining the quality of the thing.

SQC :A tool of scientific management where the control of the

quality of characteristics of a product is possible with the help

of systematic measurement in terms of numbers.


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STATISTICAL PROCESS CONTROLDefinition : Application of statistical methods at the process

control stage is known as statistical process control.

SPC is powerful collection of problem solving tools useful in

achieving process stability and improving capability.The seven major SPC problem solving tools are

1. Histogram2. Check sheet

3. Pareto Charts4. Cause and effect diagram

5. Defect concentration diagram

6. Scatter diagram

7. Control Charts


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OBJECTIVES OF QUALITY CONTROL:

To reduce company cost through reduction of the

losses due to defects.

To produce the optimum quality at minimum price.

To make inspection prompt to ensure quality control.


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BENEFITS OF SQCReduction of scraps.

Increases output.

Efficient utilization of man, machines,material & money.

Reduce wasted machine and man hour.

Ensures rapid and efficient inspection at a

minimum cost.Easy detection of faults.


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PROCESS CAPABILITYDefinition :

Minimum spread of specific measurement variation,

which will improve 99.7% of the measurement from thegiven process.

Process capability is the measured inherent variation of

the product turned out by a process.

Process capability study is carried out to measure theability of the process to meet the specified tolerances.


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FACTOR INFLUENCING PROCESS CAPABILITY

Condition of machines/equipment.

Type of operation and operational conditions.

Raw materials.

Operators skill.

Measurement instruments.

Inspectors skill.


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PROCESS CAPABILITY RATIO (Cp)

The ability of the process to meet the tolerances if at allstrength of the output that process will produce if (6 sigma >(USL-LSL)).

Cp = (USL - LSL)/6 * sigma

whereSigma = standard deviation

When Cp = 1.00: then the process is just about to meet desired

specification.When Cp< 1.00: then the process will not meet the desired

specification and is out of control.

When Cp>=1.33: then the process is under statistical controland meets desired specification.


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3

minZ

Cpk =

( )

=

XUSLZupper

=

)( LSLXZlower

PROCESS CAPABILITY INDEX(Cpk

)

Process capability indexes that measures potential for the process to generate the

output relative upper or lower specification.

WhereZmin = the smaller of the Z

upper, Z

lower

When Cpk> 1.33: the process is capable

When Cpk< 1.00: process is not capable

When Cpk=Cp: the process is centered between the upper & lower specification.


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R*DUCL 4R = R*DLCL 3R =

R*AXUCL 2x += R*A-XLCL 2x =

2d

R=

STEPS INVOLED IN THE ESTIMATION OF PROCESS CAPABILITY:

Using X (bar) and R chart information we determine the value of Cp

& Cpk

the formula for chart control limits

STEP 1:

The methods of computing control limits for a range chart based on 3 standard deviations are:

D4 and D3 are from a table of multipliers.

STEP 2:

The methods of computing control limits for an x-bar range chart based on 3 deviations is:

Upper control limits Lower control limits

Where:

A2

is from a table of factors of control limits.

STEP 3:

Standard Deviation is calculated by


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CAPABILITY ANALYSISProcess capability is analyzed with a set of statistics that

have been calculated from a set of process data and

specifications.

Capability analysis compares how a process is actually

running to its specifications.

A system is said to be capable if it is producing

approximately 100% of its output within specifications.


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Cpk

Capability Index tells how well as system can meet specification limits.

If Cpk is 1.0

The system is producing 99.73% of its output within specifications.

If Cpk is between 0 and 1.0Not all process output meets specifications.

If Cpk is less than 1.33

If Cp is less than 1.33: Variations in the process should be reduced.

If Cp is greater than 1.33: The process should be centered within itsspecifications.

If Cpk is greater than 1.33

If Cp is less than 1.33: Not mathematically possible.

If Cp is greater than 1.33: Fine tune and improve the process continuously.


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CONTROL CHARTS


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CONTROL CHART

A control chart is the graphical representation of the collected information.

THE CONCEPT OF VARIATIONS

No two items will be perfectly identical even if extreme care is taken.

Variation is a fact of nature.

The variation may are expressed in microns.

REASONS FOR VARIATION

Tools wear, Machine vibration, Loose bearings, Poor quality of raw

materials, Operators Carelessness, Untrained operator, Fatigue caused tooperator, Change in working condition/weather, Poor maintenance,

Measuring errors, etc.


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KINDS OF VARIATIONS

1. Variation Due To Assignable Causes

These can be easily traces are detected.

The variations due to assignable causes:

Difference among machines, workers, materials.

2. Chance Variation They are difficult to trace and difficult to control.

Fig: Control Chart Patterns of Chance Variation


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Assignable cause pattern of variation

Extreme variation

Extreme variation is recognized by the points fallingoutside the upper and lower control limits.

Causes: Errors in measurements and calculations Wrong setting of machine, tools


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Indication of Trend:The points on X or R chart tend to move steadily

either towards LCL or UCL, it can be assumed thatprocess is indicating a trend.

Causes: Tool wear Wear of threads on clamping devices Effects of temperature and humidity.


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ShiftWhen a series of consecutive points fall above or below the centralline, control chart is can be assumed that shift in the process hastaken place.

Causes: Change in materials Change in operator, inspector, inspection equipment Change in machine setting New operator, carelessness of the operator, etc.


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Erratic Fluctuations

Causes:

Frequent adjustment of machineDifferent types of material being processed.

Change in operator machine etc.


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NAME OF THE LINER: MWMHONING

X-bar & R chart


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AVERAGES RANGE

---------------- ------------------Upper Control Limit : 128.01042684 0.00994974

Lower Control Limit : 128.00407316 0.00000000

Average : 128.00725000 0.00436000

Sigma : 0.00211790

Center : 128.00725000

Control Limits based on all plotted groups

Control Limits shown for complete groups of size 4.

There were no observations out of control.


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Process Capability chart

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CHART1: Process CapabilityC hart: mwm 234honning - DATA1

0.0

4.8

9.6

14.4

19.2

24.0

0.00

4.60

9.20

13.80

18.40

23.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

128.002

128.003

128.004

128.005

128.006

128.007

128.008

128.009

128.010

128.011

128.012

128.013

128.014

Fitted curve is a Normal. K-S test: 0.035. Lack of fit is si gnificant.


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Calculation of Cp & Cpk


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NAME OF THE LINER: MWMCOLLAR WIDTH

X& R chart-bar


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AVERAGES RANGE

---------------- ----------------

Upper Control Limit : 7.98774933 0.01716102

Lower Control Limit : 7.97679067 0.00000000

Average : 7.98227000 0.00752000

Sigma : 0.00365289

Center : 7.98227000

Control Limits based on all plotted groupsControl Limits shown for complete groups of size 4.

There was 1 observation out of control.


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05/28/09 15:37

CHART1: Process CapabilityChart: MWM234CW - DATA1

0.0

10.0

20.0

30.0

40.0

50.0

0.00

9.20

18.40

27.60

36.80

46.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

7.970

7.972

7.974

7.976

7.978

7.980

7.982

7.984

7.986

7.988

7.990

7.992

7.994

Fitted curve is a Normal. K-S test: 0.000. Lack of fit is significant.


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NAME OF THE LINER: MWMSTEP OUTER DIAMETERX& R chart-bar


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AVERAGES RANGE

---------------- ------------------Upper Control Limit: 147.90180694 0.02382460

Lower Control Limit: 147.88659306 0.00000000

Average : 147.89420000 0.01044000

Sigma : 0.00507129Center : 147.89420000



There were 4 observations out of control.


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05/28/09 16:40

CHART1: Process Capabil ityC hart: MWM234SOD - DATA1

0.0

7.0

14.0

21.0

28.0

35.0

0.00

6.80

13.60

20.40

27.20

34.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

147.880

147.884

147.888

147.892

147.896

147.900

147.904

147.908

147.912

147.916

147.920

147.924

147.928



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NAME OF THE LINER: MWMLAND OUTER DIAMETER

X-bar & R chart


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AVERAGES RANGE

---------------- ------------------

Upper Control Limit: 143.95145634 0.02282050


Average : 143.94417000 0.01000000

Sigma : 0.00485756

Center : 143.94417000



There was 1 observation out of control.


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05/28/09 16:20

CHART1: Process CapabilityChart: MWM234LOD - D ATA1

0.0

6.0

12.0

18.0

24.0

30.0

0.00

5.60

11.20

16.80

22.40

28.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

143.928

143.931

143.934

143.937

143.940

143.943

143.946

143.949

143.952

143.955

143.958

143.961

143.964

Fitted curve is a Normal. K-S test: 0.004. Lack of fit is s ignificant.


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NAME OF THE LINER: MWMBODY OUTER DIAMETER

X-bar & R chart


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AVERAGES RANGE

---------------- ----------------

Upper Control Limit: 147.00000000 0.31319563


Center : 146.90000000 0.13724311Sigma : 0.06666667

Average : 146.90660000


There were no observations out of control


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05/28/09 17:10

CHART1: Process Capabil ityCha rt: MWM234PRE FINISHBOD - DATA1

0.0

10.0

20.0

30.0

40.0

50.0

0.00

6.40

12.80

19.20

25.60

32.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

146.800

146.815

146.830

146.845

146.860

146.875

146.890

146.905

146.920

146.935

146.950

146.965

146.980



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NAME OF THE LINER: MWMTAILEND OUTER DIAMETER

X-bar & R chart


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AVERAGES RANGE

---------------- ------------------Upper Control Limit: 133.92175391 0.09036918


Average : 133.89290000 0.03960000

Sigma : 0.01923594Center : 133.89290000



There were 3 observations out of control


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05/28/09 17:28

CHART1: Process Capability Chart: MWM234PREFIN ISHTOD - DATA1

0.0

8.0

16.0

24.0

32.0

40.0

0.00

7.80

15.60

23.40

31.20

39.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

133.790

133.805

133.820

133.835

133.850

133.865

133.880

133.895

133.910

133.925

133.940

133.955

133.970



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NAME OF THE LINER: HAFL

LAND OUTER DIAMETER

X-bar & R chart


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AVERAGES RANGE

---------------- ----------------Upper Control Limit: 110.44215559 0.04746664


Average : 110.42700000

0.02080000

Sigma : 0.01010373

Center : 110.42700000



There were no observations out of control.


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05/28/09 14:55

CHART1: Process CapabilityChart: HAFL912LOD - DATA1

0.0

7.0

14.0

21.0

28.0

35.0

0.00

6.80

13.60

20.40

27.20

34.00

CELLFREQUENCY

PERCENT

CELL BOUNDARY

110.400

110.405

110.410

110.415

110.420

110.425

110.430

110.435

110.440

110.445

110.450

110.455

110.460



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Lack of control is indicated by points falling

outside the control limit either on X-baror Rchart. It means that some assignable causesof variations are present; it not constantcause system.

After analyzing the collected data from CNCand conventional machine we found that inmany cases the value of Cpk was less thanthe minimum value. The various reasons for

this may beInput of materials

Machine maintenance

Tool wear


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Remedies

Implementation of first piece approach that is Doing it

right the first time. For every material change, tool

change and shift change online inspection is to be done by

skill supervisors.

Implementation of preventive maintenance and predictive

maintenance. For preventive maintenance check list is

done in every shift.

Training is provided to all workers regarding feeding oinput materials, online inspection etc.

Strict inspection is done for checking the hardness,

chemical composition etc.


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seminar on statistical process control on cylinder liners

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