1. Study on effect of CRIMS data on warpage simulation and possibility of using supplement CRIMS data
Speaker Name: Venkatesh Aungadu Kuppuswamy
Speaker Title : Senior Staff Materials Engineer, Motorola Solutions Inc, Plantation, FL

2. Content What is CRIMS? Overview of the Experiment
Part, Material and Process Selection
Moldflow Simulation Results
Injection Molding Study
Comparing Actual and Simulation Data
Conclusions
Acknowledgements

3. What is CRIMS CRIMS = Corrected Residual In-Mold Stress
Moldflow Simulation uses the following material parameters:
Viscosity
PVT
Thermal conductivity
Specific heat capacity
Shrinkage (CRIMS)
A1, A2 and A3 coefficients modify the parallel shrinkage
A4, A5 & A6 modify perpendicular shrinkage
A1, A2, A4 and A5 are scaling factors, where as A3 and A6 are shrinkage values

4. Overview
of the
Experiment

5. Overview of the Experiment
Moldflow DOE
Look for significant D
With CRIMS +
Without CRIMS
Molding trials
Perform measurement
Moldflow with
observed parameters
Compare predicted
with actual

6. Factors that Affect Warpage
Overview of the Experiment
Factors that Affect Warpage
Warpage
Material
Cooling Channels
Part Design
Gating
Processing
Packing Press.
Fill Time, Speed
Melt Temp.
Type
% Filler
Filler Properties
Shrinkage
Size, Location
Flow Rate
Fluid Used
Temperature
Size
Location
Number
Wall Thickness
Shape

7. Overview of the Experiment
Item
Descriptions
Part Complexity
1) Battery Cover , 2) Battery Housing , 3) Foot Ball Housing 4) Speaker Bracket, and 5) Seal Frame
Material
Crystalline filled and Unfilled
Amorphous filled and Unfilled
[ 1) Lexan 141R, 2) Cycoloy C1200, 3) Ixef 1032, 4) Grivory GV5H, and 5) Delrin 500 P]
Packing Pressure
60 % , 80 % and 100% of fill pressure

8. Part, Material and Process Selection

9. Overview of Experiment
1) Battery Cover (Flat shaped part)
2) Battery housing (Box shaped part)

10. Overview of the Experiment
3) Football housing
(Box shaped with bosses and ribs)
4) Speaker Bracket
(Thick and thin combination with weld line)

11. Overview of the Experiment
5) Seal Frame
(Long flow front with 8 flow front)

12. Overview of the Experiment
Unfilled
Filled
Crystalline
Delrin 500 ( POM)
Valox 420 **( PET+GF)
Amorphous
Cycoloy 1200(PC+ABS) and Lexan 141 R (PC)
IXEF 1032(polyarylamide + GF) and Grivory GV-5H ( PA+GF)
DOE was done in simulation to reduce molding operation
5 parts x 5 materials x 3 process conditions x 2 options for shrinkage data = 150 simulation runs
** = Additional material evaluated

13. Moldflow Simulation Results

14. Max deflection predicted
Simulation Results
Relative change in = Warpage
Max deflection predicted
With CRIMS
Without CRIMS

15. Simulation Results Multi-vari chart5 4 3 2 1 . 9 8 7 6 P a r t ( C R I M S - n o ) / d i v u l V e f
Multi-vari chart
Relative change in Warpage (with and without CRIMS) from Moldflow simulations

16. Dot Plot to Select Molding Trial
Part
Material
Packing Pressure
Battery Cover
Grivory GV-5H
80 %
Battery Housing
Ixef- 1032
100 %
Football Housing
Delrin 500P
Speaker Bracket
Ixef-1032
60 %
Seal Frame
Cycoloy C1200
Selected for Molding and CAV

17. Simulation Results
As part design complexity increases, predicted impact of CRIMS data increases
Multi-vari chart for relative change in Warpage from Moldflow simulations

18. Comments
When macros were used to create multiple Moldflow study files, check to see the log file to verify the simulation process settings are represented from process setting.
In Moldflow, after analysis, create a new anchor plane to translate all warpage values into positive co-ordinates.

19. Comparing Actual and Simulation

20. Measurement Technique
Equivalent points were measured on five samples for each part using an optical Smartscope
Averaged values are shown below:
0.747
0.866
0.774
1.88
1.025
-0.063
0.38
0.059
0.304
0.627
0.067
0.347
0.151

21. Warpage of Battery Cover
No CRIMS
CRIMS
Actual
Max:1.079
Max:1.151
1.88
0.866
0.38
CRIMS shows better prediction in Part 1
Improvement in Prediction = 17 %

22. Warpage of Battery Housing
No CRIMS
CRIMS
Actual
Max:1.1
Max:0.693
1.025
0.774
0.475
0.385
0.734
0.897
1.025
0.774
CRIMS shows better prediction in Part 2
Improvement in Prediction = 33 %

23. Warpage of Football Housing
CRIMS
No CRIMS
Actual
Max:0.4989
Max:.3700
0.2353
0.32
0.4446
0.4247
Delta = 0.46
Delta = 0 .87
Delta =
CRIMS shows better prediction in Part 3
Improvement in Prediction = 28 %

24. Warpage of Speaker Bracket
CRIMS
Actual
No CRIMS
Max:0.2543
Max:0.2793
0.1320
0.0604
0.2472
0.0798
0.1091
0.0022
0.1305
CAD dimension thickness = 6.0 mm
CRIMS shows better prediction in Part 4
Improvement in Prediction = 8%

25. Warpage of Seal Frame No CRIMS CRIMS Actual Max:0.3630 Max:0.3735
0.3581
0.3718
Delta is 0 .65
Max:
Delta is 0 .13
Delta is 0 .71 3D
Simulation with any method does not show proper prediction for Part 5
0.2473
Delta is 0 .48

26. Warpage of Seal Frame with runner
No CRIMS
CRIMS
Actual
0.2329
0.2782
Delta is 0 .44
Delta is 0 .13
Delta is 0 .53 3D
Simulation with any method does not shows proper prediction in Part 5
0.1772
Delta is 0 .32

27. Results and Discussion

28. Regression Analysis
Ideal Condition

29. Regression Analysis
In an ideal situation, where prediction and actual warpage are same the slope = 1
Slope of No-CRIMS = 0.33 and Slope of CRIMS = 0.57
The regression equation is
No Crims = Actual
S = R-Sq = 39.3% R-Sq(adj) = 35.5%
Crims = Actual
S = R-Sq = 74.2% R-Sq(adj) = 72.6%
Higher the R-Sq values better the curve was fitted

30. Box Plot
Ideal Condition

31. Probability of Good Prediction
One-Sample T: delta no crims
Test of mu = 0 vs not = 0
Variable N Mean StDev SE Mean % CI T P
delta no crims (0.0926, )
One-Sample T: delta crims
Variable N Mean StDev SE Mean % CI T P
delta crims ( , )
There is a 40% higher probability of getting accurate predictions by using CRIMS

32. Conclusions

33. Conclusions By using CRIMS data for warpage simulation
We can achieve 24 % improvement in warpage prediction
We can reduce tooling iterations to correct for part warpage
We can achieve substantial cost savings

34. Acknowledgments Marian Petrescu Steve Spanoudis Tim Dunford
Ben Nagaraj
Chris Sandieson
Dave Reiff

35. Part 2 of paper start here

36. What is the Defect?
Inability to use CRIMS simulation process due to lack of sufficient CRIMS data in material library

37. Current Status of Moldflow Material Data Base
MSI uses approximately 70 plastics materials
Remaining materials have no CRIMS data
Only “some“ materials have CRIMS data
Lexan EXL 1433T
Lexan EXL 1414
Cost for CRIMS data testing is expensive.
Testing time per batch of 4 materials is 6-8 weeks

38. Pilot Batch Experimentation
Select 10 grades to test the experimentation method
While looking for comparable material, we used the following parameters
Material composition
Filler content
MFI
Mechanical properties
Manufacturer( same is always better)

39. Pilot Batch Experimentation
Cycolac - C1200 (ABS +PC)
Altuglas V825 ( PMMA)
Lexan EXL 1414 ( PC)
Makrolon (PC)
LNP Thermocomp DF004 (PC 20% GF) LNP Thermocomp DF006 (PC 30% GF)
Lexan 920 (PC)
Bayblend FR3010 (ABS +PC)
Bayblend T45 (ABS +PC)
Xylex x7300 (PC+PET)

40. Part Design Tools Used Autodesk Moldflow Insight 2010- R2
Autodesk Moldflow Plastics Insight R2
Tools Used
Autodesk Moldflow Insight R2
One sample t test
Test for Normality
Multivari chart
Anova

41. Smart Scope with Laser Option
10 material were molded and measured for warpage with “Motorola Solution-supplemented CRIMS values”
Smart scope with routine ( Screen Shot)
Part being measured on smart scope

42. Box Plot of ‘Deltas’ of Original Cross Part
Ideal Condition
Supplemented is CRIMS Substituted
Delta = Actual warpage – Simulated Values
Found significant outliers on warpage observation on some of actual parts, which reflected in delta calculation

43. Change in Part Design Proposed part design at beginning of Project
Part design for project was changed as warpage observed had outlier on ‘cross part design’
NFL housing part used in warpage measurement, as part design was structurally good.

44. Normality Plot of Raw Data with new Part Design
No CRIMS (NC) Normality test p-value = 0.018
3D Normality test p-value = 0.296
Supplement (Sup) Normality test p-value = 0.436
Please note:Value above 0.05 means normal data

45. Box Plot and Test of equal Variance – New part
Test and CI for Two Variances: abs-del-nocrims, abs-del-Sup
Statistics
Variable N StDev Variance
abs-del-nocrims
abs-del-Sup
Ratio of standard deviations = 2.500
Ratio of variances = 6.250
95% Confidence Intervals
CI for
Distribution CI for StDev Variance
of Data Ratio Ratio
Normal (1.187, 5.264) (1.410, )
Continuous (0.478, 6.584) (0.229, )
Test
Method DF1 DF2 Statistic P-Value
F Test (normal)
Levene's Test (any continuous)
Looking at the standard deviation of No CRIMS shows that the data has unacceptability high variability, hence we are discarding no CRIMS method.
Levene test did not detect difference.

46. Test of Variance between CRIMS and 3D for new part design
Abs-del-Sup : absolute delta of supplemented CRIMS
Abs-del3D : absolute delta of 3D
Test and CI for Two Variances: abs-del3D, abs-del-Sup
Method
Null hypothesis Sigma(abs-del3D) / Sigma(abs-del-Sup) = 1
Alternative hypothesis Sigma(abs-del3D) / Sigma(abs-del-Sup) not = 1
Significance level Alpha = 0.05
Tests
Test
Method DF1 DF2 Statistic P-Value
F Test (normal)
Levene's Test (any continuous)
This shows that we can compare 3D and CRIMS
P-Value is great than 0.05, hence 3D and sup-CRIMS are identical

47. Anova to compare 3D and CRIMS for new part design
One-way ANOVA: abs-del3D, abs-del-Sup
Source DF SS MS F P
Factor
Error
Total
S = R-Sq = 15.15% R-Sq(adj) = 9.85%
Grouping Information Using Tukey Method
N Mean Grouping
abs-del3D A
abs-del-Sup A
Means that do not share a letter are significantly different
Abs-del3D : absolute delta of 3D
Abs-del-Sup : absolute delta of supplemented CRIMS
ANOVA analysis shows no statistical difference between 3D and supplemented-CRIMS

48. Box plot for new part design
Abs-del-nocrims: absolute delta of noCRIMS (Original)
Abs-del3D : absolute delta of 3D
Abs-del-Sup : absolute delta of supplemented CRIMS
Note :Ideal response is zero
From above data it is clear that the original method is less precise than proposed method. This confirms with results from project1: CRIMS methods is 29% more accurate than no-CRIMS

49. Conclusion
Supplemented CRIMS data’s warpage and show similar values as 3D
No-CRIMS warpage method showed inaccurate warpage

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