Moldex3D, Structural Analysis, and HyperStudy Integrated in HyperWorks Platform Anthony Yang Moldex3D
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
1,200+ Global Customers in various industry
Moldex3D leads the way of Technology development 2003: 1 st complete 3D CAE for plastic molding(solid) 2005: 1 st SMP/DMP 3D CAE for plastic molding 2007: propriety automatic 3D meshing (edesign) 2009: exclusive compatibility with multiple 3D CAD
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
Moldex3D Flow Analysis Moldex3D-Flow predicts melt front, weld line, air trap, short shot and process window
Moldex3D Packing Analysis Moldex3D-Pack simulates the density variation and melt flow due to material compressibility 7
Moldex3D Cooling Analysis Moldex3D-Cool simulates mold temperature, cooling efficiency, hot spot, cooling time
Moldex3D Warpage Analysis Moldex3D-Warp simulates the part warpage due to volumetric shrinkage and further help to control these defects before mold is built
Moldex3D Fiber Analysis Moldex3D-Fiber simulates the 3D fiber orientation and calculates the process-induced anisotropic properties 10
MCM Analysis in Moldex3D Moldex3D-MCM simulates the Multi-Component Molding, Insert molding and over molding process.
Exclusive Moldex3D Features
Quick True 3D Analysis in Minutes: Import STL Create Runner Set Melt Etrn Meshing Create Cooling System Run Simulation 13
Automatic 3D hybrid meshing capability
edesign: Intelligent Gate Wizard
edesign: Intelligent Runner Wizard
Accuracy - by running FULL 3D analysis High temperature resolution in runners
edesign: Intelligent Cooling System Wizard Support the ALL cooling system in 3D
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 Core (1 CPU) 2 Cores (2 CPUs) 1.00 1.00 1.00 2.01 1.89 Car Grill (elements: 713,558, R9.1 Solid-Flow Enhanced) 16-cavity Lens (elements: 1,066,448, R9.1 Solid-Flow Standard) Tray (elements: 1,422,416, R9.1 Solid-Flow Standard) Benchmark Hardware - One BoxClusterNX (www.boxcluster.com) - 4-node PC cluster - one Intel Core i7 940 CPU on each node - 12 GB DDR3 RAM on each node - Gigabit network 4 Cores (4 CPUs) 4.00 3.65 8 Cores (4 CPUs) 6.98 6.81 7.64 16 Cores (4 CPUs) 10.40 10.92 11.75 0.00 4.00 8.00 12.00 16.00 Speep Up Ratio 19
20 Moldex3D Application Examples
BASF New material development for automotive bumper Füllverhalten bei 50% Füllung Füllverhalten bei 75% Füllung
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
Moldex3D User: Connector Case The area suggested to be cored out Warpage improved by 20% after thickness cored out 23
Moldex3D User: Unilever Temperature difference :45oC ->15oC Cooling time reduced by 25% (from 5 to 3 sec) Save 4 million sec 24
FEA Integration Analysis
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)
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
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
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 76,150 to 1,866 Total material number from 3,392 to 668 29
Technology Link of FEA Interface Structure Moldex3D Simulation Ejection Application Analysis Flow Pack Cool Warp FEA- ANSYS FEA- ABAQUS FEA-MSC Nastran FEA-MSC Marc FEA LS-DYNA FEA-NX Nastran FEA- RADIOSS Warpage Mold Deform Structural Modal Analysis Drop Test Impact Paddle-Shift Core-Shift
Moldex3D-FEA Interface-Interface to Abaqus 2. Select Abaqus Solver 3. Select output meshtype 4. Select output data 5. Export.inp file 1. Click FEA Interface Icon
Tensile Bar - Wend Line strength reduction Weld Line Location 32
Fiber Orientation around the weld line Weld Line Location 33
34 Major Modulus
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
37 Thrust Pedal Fiber Orientation
38 Thrust Pedal Major Modulus
39 Thrust Pedal Minor Modulus
Thrust Pedal Model Setup Fix the pin slot Apply a force on the Pedal 40
Thrust Pedal Displacement & Stress 200lbf (900 N) Force Applied Displacement Stress isotropic anisotropic 41 0-50 mm range 0-100 MPa range
Integration between Moldex3D and HyperStudy Improving Part Quality for Injection Molding
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
Workflow between Moldex3D and HyperStudy Create an initial run and perform a preliminary analysis Copy new design factor file and call Moldex3D as the solver through script function Do Study setup, DOE setup and others setups Output response factor Finish all runs and obtain optimal results
Integrating Moldex3D and HyperStudy: DOE Study
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.
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
DOE Study: Create a DOE Study Select DOE Class Detail setting of the Study setup is shown in appendix
DOE Study: Controlled Variables Define Design Variables: Select Design variables Setup Design variable bounds and level values
DOE Study: DOE Run Table
Design of Experiments: Run Results Run Summary This chart indicates the melt temperature and packing pressure profile are the most sensitive factors Main Effects
DOE Optimal Results Variables Initial Results DOE Results Filling Time (sec) 2 2.3 Design Variables Melt Temperature ( C) 230 220 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
Integrating Moldex3D and HyperStudy: Optimization Study
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
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 %)
Settings for Objectives Objectives: Goal: Minimum Standard Deviation (SD) for Total Displacement Maximum Iterations: 20 Absolute Convergence: 0.001 Relative Convergence: 1.0%
Optimal Results History Plot History Table Optimized design factors
Optimal Results Variables Initial Run Optimal Run Filling Time (sec) 2 2.3 Design Variables Melt Temperature ( C) 230 220 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
Summary
Comparison Design Variables Variables Initial Results DOE Results Optimal Results Filling Time (sec) 2 2.3 2.3 Melt Temperature ( C) 230 220 220 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 {[0.354-(Other results)]/0.354}*100% 0% 26% 26% Initial results DOE/Optimal results Upper and lower limit values fixed to initial results
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.