Six sigma project - Gear hobbing optimization.ppt

Gear Inspection

1. Slide number: 1
Process Optimization
Process Optimization
Process selected GEAR HOBBING
Plant GNA MEHTIANA
Name of the Leader
Team Members JATINDER SINGH
SAMDEEP KAPOOR
JUJHAR SINGH
SUKHWINDER SINGH
Date of Start 10 TH OCT.

2. Slide number: 2
Process Optimization
P
Planning
A
Analyse
I
Improve
C
Control
PLANNING
ANALYSE
IMPROVE
CONTROL

3. Slide number: 3
Process Optimization
Phase – 1- Planning
No.Parameter
1 Process selected GEAR HOBBING
2 Part number selected for study450-10206
3 Machine selected for studyHB-39
4 Other similar part numbers
where the optimal setting can be
deployed
SO MANY
5 Responses Description Type of
response
(Var/Att)
Specification
SPAN SIZE
VARIATION
VARI 0.03 MAX
PCD R\O VARI 0.025 MAX
UNCLEAN
AFTER SHAV.
ATT
LEAD VARI 0.02 mm

4. Slide number: 4
Process Optimization
Phase – 1- Planning
Phase
OCT NOV Month
W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4
Plan
Analyze
Improve
Control
Project Planning
Phases Planned Start
date
Planned
Completion
date
Actual start
date
Actual
completion
date
Status
Plan 10 TH OCT 20 TH OCT 12 TH OCT 15 TH OCT O.K.
Analyze 21 OCT 10 NOV 16 TH OCT 7 TH NOV O.K.
Improve 11 NOV 20 NOV
Control 21 NOV 30 NOV

5. Slide number: 5
Process Optimization
Photograph of the part

6. Slide number: 6
Process Optimization
Phase – 1- Planning
Design Parameters identified for Optimization
A – CUTTING SPEED (mt.\min.)
B –FEED (mm\rev)

7. Slide number: 7
Process Optimization
No Parameter ( - Setting ) ( + Setting )
A
CUTTING SPEED (mt.\min) 42.39 55.38
B
FEED (mm\rev.) 1.6 2.0
Phase – 1 – Planning

8. Slide number: 8
Process Optimization
P
Planning
A
Analyze
I
Improve
C
Control
PLANNING
ANALYSE
IMPROVE
CONTROL

9. Slide number: 9
Process Optimization
- Setting + Setting
1
st
Run 0.015 0.015
2
nd
Run
0.01 0.02
3
rd
Run
0.015 0.015
Median 0.015 0.015
Range 0.005 0.005
D ( Difference Between Two
Medians )
0
d = Average of Two Ranges 0.005
D/d 0
Phase – 2 – Analyze
Step -1
SPAN SIZE VARIATION
AS THE D\d RATIO <1.25.IT SHOWS THE + SETTING PARAMETERS
ARE NOT EFFECTING THE SPAN VARIATION

10. Slide number: 10
Process Optimization
- SETTING +SETTING
1
st
Run 0.02 0.02
2
nd
Run 0.015 0.025
3
rd
Run 0.015 0.02
Median 0.015 0.02
Range 0.005 0.005
D ( Difference Between Two
Medians )
0.005
d = Average of Two Ranges0.005
D/d 1
Phase –2- Analyze
Step-1
PCD RUN OUT
AS THE D\d RATIO IS <1.25. IT SHOWS THAT +SETTING
PARAMENTER ARE NOT EFFECTING THE PCD R\O

11. Slide number: 11
Process Optimization
Phase – 2 – Analyze
Step -1
- SETTING + SETTING
1
st
Run 0.015 0.015
2
nd
Run 0.015 0.017
3
rd
Run 0.016 0.017
Median 0.015 0.017
Range 0.001 0.002
D ( Difference Between Two
Medians )
0.002
d = Average of Two Ranges0.0015
D/d 1.33
LEAD
AS THE D\d RATIO IS>1.25<3 WITH OVER LAP.
THIS SHOWS THE PARAMETER IS NOT EFFECTING THE LEAD

12. Slide number: 12
Process Optimization
Phase-2 Analyze
AFTER SHAVING WE HAVE CHECKED
THE PIECES IN BOTH - & + SETTING
AND FOUND O.K.
UNCLEAN AFTER SHAVING

13. Slide number: 13
Process Optimization
Phase – 2 – Analyze
Step -1
Conclusion: THE PREVIOUS DATA SHOWS THAT WITH
THE CHANGE IN PARAMENTERS THE RESPONSE IS
NOT EFFECTED.
HENCE WE WILL TAKE THE + SETTING AS – AND
IDENTIFY THE NEW + SETTING AND WE WILL DO
AGAIN THE SAME ANALYSIS.

14. Slide number: 14
Process Optimization
Phase-2 Analyze
Step-2
S.NO. PARAMETER - SETTING + SETTING
1 CUTTING SPEED (mt.\
min)
55.38 59.34
2 FEED (mm\rev) 2 2.25

15. Slide number: 15
Process Optimization
Phase-2 Analyze
Step-2
- Setting + Setting
1
st
Run 0.015 0.02
2
nd
Run
0.02 0.02
3
rd
Run
0.015 0.015
Median 0.015 0.02
Range 0.005 0.005
D ( Difference Between Two
Medians )
0.005
d = Average of Two Ranges 0.005
D/d 1
SPAN SIZE VARIATION

16. Slide number: 16
Process Optimization
Phase-2 Analyze
Step-2
- SETTING +SETTING
1
st
Run 0.02 0.025
2
nd
Run 0.022 0.025
3
rd
Run 0.023 0.02
Median 0.022 0.025
Range 0.003 0.005
D ( Difference Between Two
Medians )
0.003
d = Average of Two Ranges0.004
D/d 0.75
PCD RUN OUT

17. Slide number: 17
Process Optimization
Phase-2 Analyze
Step-2
- SETTING + SETTING
1
st
Run 0.015 0.025
2
nd
Run 0.015 0.030
3
rd
Run 0.016 0.030
Median 0.015 0.03
Range 0.001 0.005
D ( Difference Between Two
Medians )
0.015
d = Average of Two Ranges0.003
D/d 5
LEAD
AS THE D\d RATIO IS MORE, THIS MEANS + SETTING IS WRONG.
THIS SHOWS WE HAVE TO STICK TO PREVIOUS SETTING WHICH IS OPTIMAL.

18. Slide number: 18
Process Optimization
Phase-2 Analyze
Step-2
UNCLEAN AFTER SHAVING
AFTER SHAVING WE CHECKED THE PIECES AND
FOUND TWO PIECES WERE UNCLEANED. WHICH
IS NOT ACCEPTABLE.

19. Slide number: 19
Process Optimization
Phase-2 Analyze
Step-2
CONCLUSION : THE PREVIOUS DATA
SHOWS THAT (+) SETTING IS NOT
O.K. AS IT IS LEADING TO
REJECTION.
THE LAST (–) SETTING IS THE OPTIMAL
SETTING. WHICH IS NOT EFFECTING
THE RESPONSE.

20. Slide number: 20
Process Optimization
Phase-2 Analyze
Step-2
WE WILL FIND NOW THE OPTIMAL
SETTING WHICH WILL GIVE ALL
RESPONCES AS PER REQUIREMENT
TOOL USED : FULL FACTORIAL (AS
THE NO. OF PARAMETERS ARE TWO)

21. Slide number: 21
Process Optimization
Phase-2 Analyze
Step-2
S.NO. PARAMETER - SETTING + SETTING
1 CUTTING
SPEED (mt.\min)
55.38 59.34
2 FEED (mm\rev) 2 2.25

22. Slide number: 22
Process Optimization
Phase-2 Analyze
Step-3
THE FURTHER STUDY WAS
CONDUCTED WITH THE HELP OF
MINITAB.

23. Slide number: 23
Process Optimization
Phase -2 Analyze – Step # 4 – Factorial plots
0.000 0.005 0.010
B
A
AB
Pareto Chart of the Effects
(response is R SPAN, Alpha = .10)
A:CUTTING
B:FEED
PARETO CHART FOR SPAN

24. Slide number: 24
Process Optimization
0.0040.0030.0020.0010.000
A
B
AB
Pareto Chart of the Effects
(response is R PCD, Alpha = .10)
A:CUTTING
B:FEED
PARETO CHART FOR PCD

25. Slide number: 25
Process Optimization
0.000 0.005 0.010
AB
A
B
Pareto Chart of the Effects
(response is R LEAD, Alpha = .10)
A:CUTTING
B:FEED
PARETO CHART FOR LEAD

26. Slide number: 26
Process Optimization
Interactions plot
0.015
0.023
0.031
0.015
0.023
0.031
CUTTING SPEE
FEED
55.38
59.34
2
2.25
Interaction Plot (data means) for R SPAN

27. Slide number: 27
Process Optimization
0.0200
0.0225
0.0250
0.0200
0.0225
0.0250
CUTTING SPEE
FEED
55.38
59.34
2
2.25
Interaction Plot (data means) for R PCD

28. Slide number: 28
Process Optimization
0.031
0.023
0.015
0.031
0.023
0.015
CUTTING SPEE
FEED
2.25
2
59.34
55.38
Interaction Plot (data means) for R LEAD

29. Slide number: 29
Process Optimization
56 57 58 59
2.00
2.05
2.10
2.15
2.20
2.25
CUTTING SPEED
F
E
E
D
Overlaid Contour Plot of R SPAN...R LEAD
R SPAN
R PCD
R LEAD
0.016
0.018
0.014
0.016
0.011
0.013
Lower Bound
Upper Bound
White area: feasible region

30. Slide number: 30
Process Optimization
As we did not get any white area it shows
there is no optimal solution with minitab.
Now we will use excel to make the optimal
equation which will satisfy all the
responses.

31. Slide number: 31
Process Optimization
Phase -2 Analyze – Step # 5 – Math model
CUTTING SPEED 54.600
FEED 2.000
SPAN 0.012
PCD 0.021
LEAD 0.014

32. Slide number: 32
Process Optimization
Phase -2 Analyze – Step # 6 – Optimization
Objective of Y SPAN VAR. PCD R\O LEAD
Upper boundary of Y0.018 0.016 0.013
Lower boundary of Y0.016 0.014 0.011
Nominal value of Y0.017 0.015 0.012
FOR ALL THE RESPONCES LOWER IS BETTER
FORMULA USED
U.B.=USL-2.3*d
L.B.=USL-2.8*d

33. Slide number: 33
Process Optimization
Phase -2 Analyze – Step # 6 – Optimization
Overlaid Contour plot

34. Slide number: 34
Process Optimization
Phase -2 Analyze – Step # 6 – Optimization
Optimal Settings identified using equation

35. Slide number: 35
Process Optimization
P
Planning
A
Analyze
I
Improve
C
Control
PLANNING
ANALYSE
IMPROVE
CONTROL

36. Slide number: 36
Process Optimization
Phase – 3 - Improve
Validation using B vs C
1 Part number selected for validation
2 Better Condition
Current Condition
3 Sample size 3B,3C
4 Sample type Pieces/Batches
5 Response decided for monitoring .
6 Lot quantity (for batches)

37. Slide number: 37
Process Optimization
Data obtained during validation
Piece / Lot Better ( B ) Current ( C )
1
2
3
Phase – 3 - Improve

38. Slide number: 38
Process Optimization
Analysis - B Vs C
1 Part number selected for
validation
2 Average of B
Average of C
3 D/d ratio
4 Xb – Xc (Amount of
Improvement)
5 Sigma (B)
6 Is Xb-Xc greater than k*Sigma
(b)
Yes/No
Phase – 3 - Improve
K = 4.2

39. Slide number: 39
Process Optimization
P
Planning
A
Analyze
I
Improve
C
Control
PLANNING
ANALYSE
IMPROVE
CONTROL

40. Slide number: 40
Process Optimization
Phase – 4 - Control
Work Standard Corrected For Standardization…
NO.
EMXI
QP-WS-SP-
050(02)
10
WORKING CONDITION
1 46±5 6 5 6 46±5
NUMBER SQUEEZE WELD - I COOL - I SLOPE WELD - III
1 0 24 9 0 10
NUMBER COUNT MAX. CURR CURR - I CURR - II CURR - III
1) UPSETTING CRACK
2) UPSETTING DAMAGE
3) COMPONENTS DAMAGE
4) COMPONENTS RUST
6) COMPONENT BURR
7) HOLES OFFSET
8)UNEVEN HEIGHT
SELF INSPECTION ( PROCESS CONTROL POINT)
SL.NO.
1
2
3
4
AMENDMENT HISTORY
NOTE : INSERT TO BE CHANGED, IF BULB DIAMETER OF THE LAST COMPONENT FOUND OUT OF SPEC.
5) UNEVEN WELD
COND. DEGREE HIGH
VISUAL
VISUAL
VISUAL
HEAVY SCRATCHES
NO BURR
81350-33010 020STRIKER ASSY DOOR UPSETTING
00
WELD - II COOL - II
0 0
VISUAL DEFECTS
BURR IN CORNERS & PIERCING HOLES
BEND & DAMAGE
NO SCRATCH
NO RUST & NO CRACK
RUST & CRACKS
FREE FROM BEND &
DAMAGE
WORK STANDARD
MODEL PART NO
PROCESS
NAME
DOC .NO
ORIGINAL DATE
REVISION DATE
ONCE IN 50 NOS
PART NAME
VISUAL
16-09-2002
02.03.06
OPERATOR
INCHARGE
46±5
HOLD
4.0~5.0 KG/CM2
OPERATOR
CHOWEL-ASR-150
ONCE IN 50 NOS
OPERATOR
ONCE IN 50 NOS
OPERATOR
ONCE IN 50 NOS
REASONSI NO REV NO
MACHINE NAME
DESCRIPTION CONTROL SPEC
CHECKING
METHOD
FREQUENCY
AIR PRESSURE
DATE
1 01 30.05.03 DOCUMENT NUMBER ADDED
SKETCH
History of Problem
S
E
T
T
I
N
G
TIPDRESSING
NUT / JOB
400 1-S
2
Q / CHK FREQ TRAY QTY
50 200
CRATE
BIG YELLOW