SPE Annual Blow Molding Conference 2017

FINITE ELEMENT BASED REALISTIC SIMULATION
FOR PACKAGING QUALIFICATION
Arindam Chakraborty, PhD, PE (TX, CA)
Partner, Virtual Integrated Analytics Solutions (VIAS)
Houston, TX
www.viascorp.com
October 2-4, 2017

Agenda
© 2017 Virtual Integrated Analytics Solutions Inc.
• VIAS Overview & Capabilities
• Realistic Simulation
• Design Optimization
• Manufacturing Simulation by Type of Packaging
• Case Studies
• Simulation Automation
• Concluding Remarks
2

VIAS Overview
• Multiple Industry Experience
• Presence in Houston, Chicago, Cincinnati, San Francisco
• Team consists of Ph.D. and Masters in Solid Mechanics, Fluid
Mechanics, Materials and Corrosion, Numerical Analysis,
Statistics, Optimization and Reliability
• Solution partner of Dassault Systèmes - SIMULIA products –
Abaqus, Isight, fe-safe, Tosca, CATIA, DELMIA, 3DEXPERIENCE
• Provide Virtual Design Experience through Collaboration and Data
Analytics – Automation and Customization
• Provide 3D Printing and AM Process Simulation Services
4
Engineering
Consultancy
Training
Automation
&Customization
Software

Our Technical Capabilities
Design by Analysis and
Validation using
Simulation
Durability Analysis
Optimization and
Reliability
Multi-physics
Simulations (CFD,
Thermal Analysis)
Composites and Rubber
Modelling
Delamination, De-
bonding and Crack
Propagation
Plastics Simulation –
Process & Application
Simulation Automation /
Plugin
Materials Testing &
Corrosion
Additive Manufacturing
Simulation
5

What is Realistic Simulation?
© 2017 Virtual Integrated Analytics Solutions Inc. 7
• “Realistic Simulation” is a simulation that is physically realistic and “life
like” in every way
• Enables engineers to create life like models that will behave similarly to the real part / product.
• Typically, start with simple models and increase the complexity, model size and the physics as
confidence in simulation results increases.
• Enables building up expertise and most importantly find value in the simulation
Courtesy Mechanical Design and Analysis Corporation, 2010 SCC

Why Realistic Simulation?
• Designers often create innovative concepts,
but retreat to conventional shapes due to
▪ Limited time to market
▪ Limited and costly resources for
physical prototyping and testing
• Simulation enables designers to virtually
test new innovative concepts
• Reduces design time and the expensive
cost of physical testing
• Lowers material cost and improves
sustainability by light-weighting
• Reduces damage cost during production
and transport by eliminating bad designs
quickly

Benefits of Simulation / Virtual Testing

Overview of Consumer Goods Experience:
Consumer Goods
Products
Baby Products
(Diapers)
Flexible
Packaging
Bottles
Blow Molding
Top Load, side
Load
Pressure,
Vacuum load
Retort,
Sterilization
Labeling
Drop Test
Dispensing
Shelf Life
Capping
Chips (Pringles)
Hygiene Products
(Towels, wipes
etc)
Process
Conveying
Web Handling
 Complexity of materials
 High to extreme deformation
 Highly non linear contact
 Complex physics-first principles
 Automation and Deployment

Challenges in Modelling Plastics and Polymers
• Polymeric materials are complex in their mechanical
behavior
• Exhibit large strain, anisotropic and irreversible
response which is often accompanied by stable
localized necking behavior
• Modelling failure and fracture a challenge
• Simulation software/ models must deal with severe
non-linearities
Blow Molding (Plastic Bottle)
Necking (ASTM D638 Hyperelastic)
Viscoelastic Rubber
Seal Insertion
Fracture/
Delamination
Viscous Paste

FEA for Thermo-Mechanical Response of Plastics
• Initial FEA simulation codes were largely
developed for heavy duty components and were
concerned predominantly with metals
• Plastics/ Polymers have much higher strains and
large displacements in comparison to metallic
materials
• Modern FE codes accommodate large strain
formulations, including hyperelasticity,
viscoelasticity
• Non-linear codes (Abaqus) perform better since
they are designed to accommodate large
deformations
• Physically Motivated Models - material response from a viewpoint of the microstructure
• Phenomenological Models - material response from the viewpoint of continuum
mechanics

Capturing Material Property Change - Example from
Polymer AM Simulation
Part level simulation with support structures
Tool Path information from slicer (GrabCad)
Warping Effects – Mobius Arm
Material Orientations during layup
Abaqus Thermal Model vs
Thermal Imaging Data

Multi-scale Homogenization

MANUFACTURING SIMULATION
BY TYPE OF PACKAGING

Thermoforming
Food Plastic Tray Forming
• Challenges:
• Manufacturability of the new pack with a
uniform thickness
• Values:
• Capture realistic thickness distribution of the
pack after thermoforming
• Optimize shape to get the uniform thickness
distribution
• Investigate how to get the closer thickness to
the target uniform thickness
16

Plastic Injection
• Challenges:
• Identify optimal injection locations
• Mold rework due to design changes
• Value:
• Predict and avoid manufacturing defects
• Predict pack / mold design impact on manufacturing
• Eliminate costly mold re-work
• Improve product appearance & quality
• Decreasing time to market and optimizing production cycle times
• Optimize the injection process to produce desired final package

• Challenges:
• Manufacturability of new design
• Expensive physical testing to validate the design
• Values:
• Quickly eliminate bad designs
• Reduce cost of prototyping
• Capture realistic thickness distribution of the bottle after blow molding
• Optimize shape to get the thickness distribution that meets all the critical
criteria (Top loading force, mass, …)
• Optimize preform shape to produce desired final thickness distribution
Blow Molding

Cap On/Off with torque
Threaded Cap Movement
• Challenges:
• Extending the shelf-life of end-products
• Easy to use sealed cap
• Customization according to the requirements of
specific brand
• Values:
• Design functional & light weighted closures
• Predict sealing performance
• Predict over stressing of the parts
• Predict torque requirements
• Simulate the Opening/Closing Phases & the Result
stress on the Cap
Predict the Required Torque to Open / Close the Container
19
Capping

Flexible Packaging
Venting Seal
Bar Design
Retort Sterilization
• Challenges:
• Understand Cooking & Sterilization Processes
• Flexible Package Leakage
• Sustainability
• Values:
• Optimize the “Venting Seal Bar” design to have a
container (pouch) strong enough to withstand high
temperatures & pressures
• Capture the sterilization process simulation behavior
• Predict pack sealing
• Predict the most suitable design
Predict High Stress Areas Film Tension Control

Bottle-Fluid Interaction
Stresses in Bottle Fluid Velocity Vectors
• Challenges:
• Package might be too thin to sustain internal
pressure during/after filling
• Filling the package too fast causes foaming, air
bubbles
• Values:
• Predict if package is too thin and deformation
after filling in
• Find appropriate filling speed to avoid foaming /
air bubble formation
• Avoid product waste at production line
• Bottle / Pouch Filling: Model the liquid pouring
process and the interaction with bottle/ pouch
surface
Fluid
Sloshing
Surface Stress in
Pouch
21
Filling

Courtesy:
P&G
• Challenges:
• Automated plan: bottle to remain standing while
traveling through conveyer belt
• Expansive cost of production line blockage
• Physical testing of conveying systems which is time
consuming
• Values:
• Reduce damage cost by eliminating bottle tilting,
bottle shingling, bottle denting, content spilling, line
blockage
• Optimize conveyer line topology, predict line speed
• Reduce overall production cost & time
• Reduce production cost & avoid assembly line
blockage
• Predict if the bottle passes the assembly line without
any liquid leak
Conveying

Reliability Based Optimization
• Flexible automated design exploration strategies combining DOE,
surrogate models, multi-objective optimization and stochastics.
Y1
Constraint
Boundary
Y2
Initial Design
Search for solution
Optimization
Robust/Reliability Design
(Quality Engineering)
Feasible Infeasible
(safe) (failed)
X2
X1
DOE:
Critical Factors
And Initial Design

Design Optimization
• Process Integration and Design Optimization
• Integrate Disparate Simulation Tools without programming
• Automate Simulation Process Flows

• Generate Thousands of Predicted Designs
• Vary multiple Input Parameters
• Compute Simulated Outputs
• Output Parameters linked to downstream models
• Choose the Best Design or Range of
Alternates
• Find Best Predicted Design via Ranking Engine
• Quick & Intuitive Design Comparison
• Transparent and Verifiable Decisions
Capacity Pallet Loading CostShelf LifeCrush FEA Analysis
Digital Continuity and Collaborative Trade-Off Analytics
Example
• Capture Multi-Discipline, Predictive Process
26

Case Study: Residual Stress
• Residual stresses may be introduced into plastic parts produced by the injection
molding process
▪ As a result, the part may warp or experience a reduction in strength
▪ The design of an injection molded product can be improved if the effect of
residual stresses on the final shape and performance of the product are predicted
accurately

Case Study: Gas Diffusion
• PET beverage bottles are ~40 times more efficient than HDPE, but CO2 and O2
nevertheless trickle slowly through its walls over time.
• As a result, the gas concentrations over time reduces effecting shelf life
▪ OTR is simulated using mass diffusion procedure – same as used in electronics
industry 9Moisture absorption) to nuclear (H2 embrittlement)
▪ Bottle’s geometry – wall thickness profile due to manufacturing process and the
bottle shape and material characteristics – crystallinity, diffusivity, and solubility is
considered.
Courtesy: Coca-Cola

Case Study :Blow Molding - Plastic Bottle
• Challenge
▪ Check if a new bottle design can be
manufactured
▪ Expensive prototype cost to validate a
new bottle design
• Simulation
▪ Incorporate the geometry of new bottle
design and physical phenomena
involved in the Blow Molding process
• Value
▪ Quickly eliminate bad designs and
drastically reduce cost of prototyping
and testing
▪ Capture realistic thickness distribution
of package after blow molding
▪ Optimize preform shape to produce a
desired final thickness distribution

Case Study: Thickness Optimization
• Top Load (TL) thickness optimization
• Vary thickness smoothly along bottle height
▪ Use 5 thickness values as optimization
parameters
• Constraints – minimum TL force > 100 N
• Optimization target – minimize mass

Case Study: Thickness Optimization (Cont.)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 50 100 150 200 250
Target Thickness
STH
• Mass: 43% reduction
• Meets target Top Loading Force

Benefits of Process Automation
Provides automated
environment for experienced
analysts
Provides
advanced analysis functionality to
non-FEA users
Customized
application
Customized versions of
Abaqus provide an
effective analysis tool
for a wide range of
user expertise …
• Automate repetitive tasks
➢ Model building
➢ Job Submission and Monitoring
➢ Post Processing
➢ Prescribe Default Behavior
• Extend functionality
• Enhance the interface
➢ Graphical
➢ Non-graphical

Benefits of Process Automation
Concept Design Validation Manufacture
Conceptual analyses Design Studies Late design changes
Automated FEA Processes Impact Here
• Process automation can deliver
➢ Repeatability, Productivity, and Quality to proven workflows and methods
throughout the design and validation process
➢ Customized solutions for specific applications, via customer-preferred
interface (web, familiar user interface, PLM system)

Abaqus Scripting Features
• Abaqus can be customized using
➢ Abaqus API (Application Programming
Interface), User sub-routines
➢ Fortran, C ( analysis product customization)
➢ Python, C++
▪ Non Analysis product customization
▪ Python is distributed with Abaqus
software
➢ GUI Toolkit (Fox Toolkit)
▪ Extension of Python language
▪ Extremely Helpful for GUI customization

Highlights for Simulating Plastics
• Analysts need to always keep the following in mind
– Benchmark materials often and early - do not just rely on published properties, calibrate
material parameters using global response, if needed
– Verify that solver and element mesh can accurately reproduce behavior in the lab
– Reporting strains as well as stresses – due to large deformation and non-linear behavior
reporting strains are important
– Use high-quality elements – Four-noded tetrahedral, the mainstay of many FE codes, is
artificially stiff when subjected to high deformations. Using these low-quality elements can
affect the accuracy of results (avoid them if possible)
– Validation – always compare with physical data

Summary
• In summary, simulation that mimics realistic scenarios helps by
– Increasing design robustness and confidence in product quality
– Decrease in development costs and improvement in product performances
– Predict the most suitable manufacturing process for component
– Providing a best possible starting point for futuristic and more advanced designs
– Functional design and optimization

Thank you
40

SPE Annual Blow Molding Conference 2017

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to SPE Annual Blow Molding Conference 2017

Similar to SPE Annual Blow Molding Conference 2017 (20)

More from Arindam Chakraborty, Ph.D., P.E. (CA, TX)

More from Arindam Chakraborty, Ph.D., P.E. (CA, TX) (18)

Recently uploaded

Recently uploaded (20)

SPE Annual Blow Molding Conference 2017