In recent years, with the increasing variety, complexity, and precision requirement on plastic products, CAE tools have been widely used for solving product design and manufacturing issues. The structural designs or molding process parameters for products can be optimized efficiently through CAE analyses. Plus the reliable and correct verification with experiments, the directions or guidance in designs or process condition settings can be provided prior to the real moldings. However, sometimes it is not efficient to find an optimized set of parameters through traditional CAE analyses. A novel integration between Moldex3D and HyperStudy allows for more quick and efficient parameter optimization which will save time, increase product quality, and increase productivity. Also, traditional CAE analyses do not consider the molding properties influence on structural analysis, such as material property variations caused by fiber orientation and residual stresses. Accordingly, an integrated technology is proposed to bridge molding and structural analysis. Through the integration of Moldex3D and structural analysis in HyperWorks platform, the important effects from molding process can be transferred to structural analysis for more accurate and realistic predictions of the product behaviors. This integration provides a virtual product development platform for users to increase profits as well as enhance productivity.

1. Moldex3D, Structural Analysis, and HyperStudy
Integrated in HyperWorks Platform
Anthony Yang
Moldex3D

2. CoreTech System and Moldex3D
 The world’s largest injection molding CAE ISV
 80% experienced engineering professionals
 50% of employees involved in R&D activities
 9 global offices, local support from Michigan
 1,200+ global customers
 6,000+ industrial projects validation

3. 1,200+ Global Customers in various industry

4. Moldex3D leads the way of Technology development
2003: 1st complete 3D CAE for plastic molding(Solid)
2005: 1st SMP/DMP 3D CAE for plastic molding
2007: propriety automatic 3D meshing (eDesign)
2009: exclusive compatibility with multiple 3D CAD

5. How Moldex3D Can Help?
 Aesthetics and dimensional concerns
 Weld line, air trap, flow mark
 Flow balance and part weight
 shrinkage and warpage control
 Fiber orientation
 Being more competitive
 Cycle time reduction by removing
hot & cold spots
 Mold structure optimization
 Reduce mold trial & tooling cost
 Reaching Lean Production
 Injection conditions optimization
 Clamping force reduction
 Machine selection

6. Moldex3D Flow Analysis
 Moldex3D-Flow predicts melt front, weld line, air trap,
short shot and process window…

7. Moldex3D Packing Analysis
 Moldex3D-Pack simulates the density variation and melt
flow due to material compressibility
7

8. Moldex3D Cooling Analysis
• Moldex3D-Cool simulates mold temperature, cooling efficiency, hot spot,
cooling time …

9. Moldex3D Warpage Analysis
 Moldex3D-Warp simulates the part warpage due to volumetric shrinkage
and further help to control these defects before mold is built

10. Moldex3D Fiber Analysis
 Moldex3D-Fiber simulates the 3D fiber orientation and calculates the
process-induced anisotropic properties
10

11. MCM Analysis in Moldex3D
 Moldex3D-MCM simulates the Multi-Component Molding, Insert molding
and over molding process.

12. Exclusive Moldex3D Features

13. Quick True 3D Analysis in Minutes:
Create Runner Create Run
Import STL Meshing
Set Melt Etrn Cooling System Simulation
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14. Automatic 3D hybrid meshing capability

15. eDesign:
Intelligent Gate Wizard

16. eDesign:
Intelligent Runner Wizard

17. Accuracy - by running FULL 3D analysis
High temperature resolution in runners

18. eDesign:
Intelligent Cooling System Wizard
Support the ALL cooling
system in 3D

19. SMP/DMP Parallel Computing with excellent
acceleration ratio
Moldex3D R9.1 Solid-Flow Parallel Computing Performance on an Intel Core i7 Cluster - Speed Up Ratio
1.00 Car Grill (elements: 713,558, R9.1 Solid-Flow Enhanced)
1 Core (1 CPU) 1.00 16-cavity Lens (elements: 1,066,448, R9.1 Solid-Flow Standard)
1.00 Tray (elements: 1,422,416, R9.1 Solid-Flow Standard)
Benchmark Hardware - One BoxClusterNX (www.boxcluster.com)
2.01 - 4-node PC cluster
2 Cores (2 CPUs) 1.89 - one Intel Core i7 940 CPU on each node
- 12 GB DDR3 RAM on each node
- Gigabit network
4.00
4 Cores (4 CPUs) 3.65
6.98
8 Cores (4 CPUs) 6.81
7.64
10.40
16 Cores (4 CPUs) 10.92
11.75
0.00 4.00 8.00 12.00 16.00
Speep Up Ratio
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20. Moldex3D Application Examples
20

21. BASF – New material development for automotive
bumper
Füllverhalten bei 50% Füllung
Füllverhalten bei 75% Füllung

22. Moldex3D:Danfoss
 Improve design from one
material molding into two
color molding
 Reduce cycle time of the
molding by 43%. Shorten
time to the market.
 Reduce material cost by
11% via product geometry
optimization
22

23. Moldex3D User: Connector Case
The area
suggested to
be cored out
Warpage improved by 20% after
thickness cored out
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24. Moldex3D User: Unilever
 Temperature difference :45oC ->15oC
 Cooling time reduced by 25% (from 5 to 3 sec)
 Save 4 million sec
24

25. FEA Integration Analysis

26. What can Moldex3D-FEA Interface to Abaqus do?
• To consider the process-induced variation during the processes
– Mesh output
• Original / deformed mesh
• Mesh mapping
– Material properties output
• Anisotropic properties
• Fiber Orientation tensor
– Result output
• Thermal/Residual stress
• Temperature (Part/Mold)
• Pressure history (Part/Mold)

27. Moldex3D-FEA Interface－Anisotropic material
properties
• Based on the fiber orientation and proper micro-mechanics models,
Moldex3D-FEA Interface can output
– Stiffness matrix
– Thermal expansion coefficient
27

28. Moldex3D-FEA Interface Orientation tensor (for
Digimat)
• Orientation tensor can be output to composite modeling software
(Digimat) to perform more accurate micro mechanical properties
calculation

29. Moldex3D-FEA Interface－Material Reduction
• Material Reduction
– Moldex3-FEA Interface can reduce the anisotropy scale by homogenizing the
similar anisotropic properties so as to improve the computational efficiency
Total material number from Total material number from
76,150 to 1,866 3,392 to 668
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30. Technology Link of FEA Interface
Structure
Moldex3D Simulation Ejection Application
Analysis
Flow Pack Cool Warp
FEA-
ANSYS
Warpage
FEA-
ABAQUS Mold Deform
FEA-MSC Structural
Nastran
FEA-MSC Modal Analysis
Marc
Drop Test
FEA
LS-DYNA Impact
FEA-NX Paddle-Shift
Nastran
Core-Shift
FEA-
RADIOSS

31. Moldex3D-FEA Interface－Interface to Abaqus
3. Select output meshtype
2. Select Abaqus Solver
4. Select output
data
1. Click FEA
Interface Icon
5. Export .inp file

32. Tensile Bar - Wend Line strength reduction
Weld Line Location
32

33. Fiber Orientation around the weld line
Weld Line Location
33

34. Major Modulus
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35. Tensile Bar – Stress
30MPa Load Applied
Yield at 80 Mpa
47 MPa
30 MPa Load
Yield at 80 Mpa
79 MPa
30 MPa Load
35 0-80 MPa Range displayed

36. Thrust Pedal – Filling Animation
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37. Thrust Pedal – Fiber Orientation
37

38. Thrust Pedal – Major Modulus
38

39. Thrust Pedal – Minor Modulus
39

40. Thrust Pedal – Model Setup
Fix the pin slot
Apply a force on
the Pedal
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41. Thrust Pedal – Displacement & Stress
200lbf (900 N) Force Applied
Displacement Stress
isotropic
anisotropic
41 0-50 mm range 0-100 MPa range

42. Integration between Moldex3D and
HyperStudy
Improving Part Quality for Injection
Molding

43. Introduction: Moldex3D and HyperStudy
• Moldex3D
• Moldex3D is the world leading CAE product for the plastics injection molding
industry
• HyperStudy
• HyperStudy is software to perform Design of Experiments (DOE), optimization,
and stochastic studies in a CAE environment
• HyperStudy is a member of the HyperWorks suite of software products
• Benefits of Moldex3D and HyperStudy Integration
• Users can employ HyperStudy to perform a series of Moldex3D analyses
systematically for improving part qualities
• Process conditions can be optimized automatically
• Moldex3D supports all study types for HyperStudy

44. Workflow between Moldex3D and HyperStudy
Create an initial run and perform a preliminary analysis
Copy new design factor file and Do Study setup, DOE setup and
call Moldex3D as the solver others setups
through script function
Output response factor
Finish all runs and obtain optimal results

45. Integrating Moldex3D and HyperStudy:
DOE Study

46. Case Study
• An injection molded part from a speed meter shows potential warpage
problem from preliminary Moldex3D analyses.
• Dimension: 400 x 126 x 76 mm
• The target is to reduce warpage through optimizing process conditions
with HyperStudy and Moldex3D using DOE study.

47. Design of Experiments Conditions
• DOE Class: 9-run Fractional Factorial
• Initial Design Variables
• Filling Time: 2 sec
• Melt Temperature: 230˚C
• Mold Temperature: 70˚C
• Packing Pressure Profile %: 75%
• Design Variables
• Number of Variables: 4
• Filling Time: 1.7, 2, 2.3 sec (3 levels)
• Melt Temperature: 220, 240˚C (2 levels)
• Mold Temperature: 65, 75˚C (2 levels)
• Packing Pressure Profile %: 70, 75, 80 % (3 levels)
• Response Variable
• Standard deviation for total displacement (mm)
• In other words, the target is to have as uniform displacement as possible

48. DOE Study: Create a DOE Study
Select DOE Class
Detail setting of the Study setup is shown in appendix

49. DOE Study: Controlled Variables
• Define Design Variables:
Select Design variables
Setup Design variable
bounds and level values

50. DOE Study: DOE Run Table

51. Design of Experiments: Run Results
Run Summary
This chart indicates the melt
temperature and packing
pressure profile are the most
sensitive factors
Main Effects

52. DOE Optimal Results
Variables Initial Results DOE Results
Filling Time (sec) 2 2.3
Melt Temperature (˚C) 230 220
Design Variables
Mold Temperature (˚C) 70 65
Packing Pressure Profile (%) 75 80
Response Variable SD for Total Displacement (mm) 0.354 0.262
• HyperStudy DOE study will lead to minimum standard deviation (SD) for Total
Displacement. It implies that the part deformation will become more uniform in
general.
Initial Results DOE Results

53. Integrating Moldex3D and HyperStudy:
Optimization Study

54. Create an Optimization Study
• The same optimization target can be achieved by employing an
Optimization Study. For example: Adaptive Response Surface Method
(ARSM)
Select Optimization Engine
Other optimization engines available in
HyperStudy are

55. Optimization Study: Define Design Variables
• Define Design Variables:
• Filling Time (Range: 1.7, 2.3 sec)
• Melt Temperature (Range: 220, 240˚C)
• Mold Temperature (Range: 65, 75˚C)
• Packing Pressure Profile % (Range: 70, 80 %)

56. Settings for Objectives
• Objectives:
• Goal: Minimum Standard Deviation (SD) for Total Displacement
• Maximum Iterations: 20
• Absolute Convergence: 0.001
• Relative Convergence: 1.0%

57. Optimal Results
History Plot
History Table
Optimized design factors

58. Optimal Results
Variables Initial Run Optimal Run
Filling Time (sec) 2 2.3
Melt Temperature (˚C) 230 220
Design Variables
Mold Temperature (˚C) 70 65
Packing Pressure Profile( %) 75 80
Response Variable SD for Total Displacement (mm) 0.354 0.262
• Recommended optimal results will lead to the minimum standard deviation (SD)
for Total Displacement. It means that the part deformation will become more
uniform in general.
Initial Results Optimal Results

59. Summary

60. Comparison
Variables Initial Results DOE Results Optimal Results
Filling Time (sec) 2 2.3 2.3
Melt Temperature (˚C) 230 220 220
Design Variables
Mold Temperature (˚C) 70 65 65
Packing Pressure Profile( %) 75 80 80
Response Variable SD for Total Displacement (mm) 0.354 0.262 0.262
Warpage Improvement
0% 26% 26%
{[0.354-(Other results)]/0.354}*100%
Initial results DOE/Optimal results
Upper and lower limit values fixed to initial results

61. Conclusion
• The integration between Moldex3D and HyperStudy helps users to find out the
optimal process conditions for injection molding systemically.
• Both DOE Study and Optimal Study can reduce maximum displacement from 1.4
mm (initial design) to 1.0 mm (optimal design), which is a 27% improvement.
• According to the DOE Study results, melt temperature is the most important and
filling time is the least important factor for warpage of this case.
• Both DOE Study and Optimization Study can reduce warpage by 26%. However,
please note it’s likely to find different optimization studies lead to slightly
different optimized results.