application of computational techniques during the development of packaging and medical devices

Application of Computational Techniques during the development of Packaging and Medical Devices (IAG 05)

ADarren Hodson PAR&D, AZC October 2005

Theme• Brief outline of the development

process.• Phases of the product cycle.• Key messages in today’s business environment.

• Applicable computational techniques and rationale for use.

• Modelling approaches and methods.

• Case study• Simulating and verifying material behaviour for medical

devices.• Multi Component systems.• Material investigation.


Development Process

Phases of the product development cycle

Concept Feasibility Design Verification Validation Launch

• Reduce product development cycle time

• Maintain or enhance quality standards


Development Process:- Key message

The Product from a Patient Perspective

To meet shorter timelines, we must:-• Show as early as possible that our product will

meet the desired drug performance parameters “Therapeutic effect”.

• Show that the patient can use or interact in the desired manner with the device or packaging.


Development Process:- Key message

Product development cycle

To deliver the product, we must:-• Show as early as possible that our product can be

manufactured in the required volumes at the desired levels of quality and cost.

• Show as early as possible that our product will meet the desired mechanical, environmental and user performance parameters.

• Commit to plant investment as early as possible but only when the design has been frozen.


Culture of Up-Front Simulation:-

• Who can perform the simulation.• Resistance in the Ranks.• Doing it right.

”Fereydoon Dadkhan, Delphi Delco Electronics Systems” Ansys solutions Volume 4 No. 1

Time

Res

ourc

e

Using CAE

Design>build>test>analyse

Design>build>test


Structural Mechanics

• Why Structural analysisFor Medical Devices and Packaging

methods can be used to :-

• Simulate highly non-linear systems.• Rapidly compare design solutions

and provide design guidance.• Verify the design principals of

complex components and inter component interactions

• Verify performance and design sensitivities.

• Verify performance variance in production.

Typical Metering Valve


Structural Mechanics

Sheldon Imaoka Ansys Inc

• Considerations• Simplicity with

increasing complexity.

• Validate each step of the analysis.

• Expected Output• Understanding of

interactions, (forces, deflection)

• Component Optimisation


Strategy for use of Advanced Techniques

• Tools best used as comparative tools when system are highly complex and time is limited however confidence in methods must be attained.

• Simulation is an attempt to idealise a very complex non-linear world. We can’t model the Universe.

• Assumptions and approximations have to be made.• Validation is just as important as the simulation itself.


Development Process

Phases of the product development cycle

Concept Feasibility Design Verification Validation Launch

Simplistic Calculations

Advanced Techniques.

Simplicity and Rapid Comparative Analysis

IncreasedComplexity,

Sensitivities andVariance

Validate methods

FinaliseVerification &Validation

Phases for Simulation:- The Device or packaging

Plant InvestmentDecisions


Case study 1:- Simulating Medical Devices

Development of a polymeric spring

Moment Vs Rotation (Target)

0

5

10

15

20

25

0 2 4 6 8 10 12 14

Rotation DegreesM

omen

t Nm

m

• Minimise stress/strain.

• Match suggested Torque profile.

• Optimise the design.

• Assess variance of performance and reliability of the design.

• Assess the sensitivities of the design’s performance.

• Compare theoretical results to experimental.


Case study 1:- Assumptions

• Linear isotropic materials• Are they?

• Application of boundary conditions, point loads, pressure distributions.

• Are they understood?

• How’s the component manufactured?• Variance

– Material Properties and processing– Dimensionality– Assembly conditions


Case study 1:- Materials

Comparison of Materials

0

50

100

150

200

250

300

350

400

450

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

StrainSt

ress

MPa Plastic 1

Plastic 2Steel

• Polymers are highly Non-linear and Anisotropic

• Process dependent properties• Maximum Strain level important,

hydrostatic stress?• Avoidance of creep

Stress Strain profile For Plastic 2

0

10

20

30

40

50

60

70

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Strain MPa

Stre

ss

• Linear materials are easier to analyse !

Melas material simulation model used


Design Evolution:- “In Silico”

Comparison of Spring rates

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14

Rotation Degrees

Mom

ent N

mm

Bspring v8 LinearBspring V8 nonlinNew spring v1New Spring 2Target rateNew2_nonlinearNew_3_nonlinNew_4 nonlinearnew 5 nonlinVersion 6

Initial design

Final design

• Rapid comparison of designs

• Rapid prototype and test


Design Assessment:- PDS variance analysis

• Gaussian distributions assumed.

0.051Mfact0.0081.3mmThickness0.0080 mmOffset 20.0080 mmOffset 10.0080.1mmAssembly

VarianceNominal

Variance of Stress Strain profile

0

10

20

30

40

50

60

70

80

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Strain MPa

Stre

ss Nominal MaximumMinimum



152SimulationsMelasMaterial Model

LHSMonte Carlo

-Material

3915Elements

1 Hour paRun Time



Graph of variance of Torque Profile from Nominal

-30

-20

-10

0

10

20

30

40

50

0 1 2 3 4 5 6 7 8

Degrees Rotation

Varia

nce

%normalised minnormalised maximum

• Number of simulations?

• Accuracy of simulations?

• Acceptable performance?

• Controllable process?

• Assembly?


Analysis convergence:- Cost factors

12

34

Solution time

Number of elements

Number of nodes

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Solution number

Analysis Convergence

Solution timeNumber of elementsNumber of nodes

• Meshes that are too coarse may not yield sufficiently accurate results.

• Time vs Accuracy, Gain ?Accuracy

Cost,Time

0

10

20

30

40

50

60

70

80

90

1 2 3 4

Strain %Stress %Moment %Size %


Case study 1:- Experiment vs. FEA

• Understanding of Material ?• Experimental Technique ?• Component Interactions ?• FEA technique ?

Initial Experimental data

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6

Rotation Degrees

Mom

ent N

mm Experimental Data

Experimental DataFEA Data

Hysteresis

Pre Load @ 3 Degrees

Loss of Pre load


Case study 1:- Creep adjusted FEA data

Creep adjusted FEA data+/- 19% variance

0

2

4

6

8

10

12

14

16

18

0 1 2 3 4 5 6

Rotation Degrees

Mom

ent N

mm

feaExperimental data after 168 hrsExperimental dta after 25 hrs


Case study 1:- Simulation of Primary Creep

)/()1(1

.432. TCCC

cr etC −+= σε

32.1

.CC

cr tC σε =

crε

Time10 hrs

Creep Curves

0

0.005

0.01

0.015

0.02

0.025

0 100 200 300 400 500 600 700 800 900 1000

time hours

cree

p st

rain

3mpa

6mpa

9mpa

12mpa

15mpa

18mpa

21mpa

24mpa

27mpa

30mpa

Primary Creep Eqn Time Hardening:-

04 =C


Case study 1:- Further Work

• Improve understanding of Hysteresis.

• Explore use of appropriate creep equations.

• Explore use of alternative material models. Viscoelastic !!


Case study 2:- Multi Component System’s

Idealised geometry:- Compound cylinders

• Devices consist of multi-component systems

Key Issues• Combination of materials,

non-linear? • Variance of

performance ?• Reliability of the

design?• Stress Relaxation and

Creep?

Loading+/- Tol

Loading



• “Little information is available for predicting the behaviour of multi-component systems.”

Further issues• Agreement on thermoplastic material

models?• Agreement of simulation methodology?

• Rapidly changing stress state ?

T.Hyde et al Journal of Strain Analysis, V 31 No. 6 Nov 1996.

Classical Case



• Lame’ equations can be used to describe static case for linear elastic materials


Case study 2:- PDS Variance analysis

ParametersNominal Tolerance STDEV

RD1 6.0 0.05 0.016667RD2 7.0 0.05 0.016667RD3 7.0 0.05 0.016667RD4 10 0.05 0.016667RD5 10.0 0.05 0.016667RD6 11.5 0.05 0.016667Internal pressure 10.0 1 0.333333

R

MethodRun Time per sim (Sec) 198Materials LinearNumber of simulations 250.0Monte Carlo LHSRsponse surface Simulations 10000.0

• Variance of performance?

• Location of maximum stress?

• Run time approx 10 Hours



LHS Results Response surface fit• Variance of performance easily assessed.

• Peak stress evaluated.

• Number of core simulations is important.



16

• Correlation between output parameters ?

• How does creep or relaxation influence this peak stress over time?

PDS simulation Results LHSNominal Tolerance STDEV

Hoop Stress location 1 0.382 39.423 13.141Hoop Stress location 2 -0.985 34.215 11.405Hoop Stress location 3 -0.705 2.923 0.974Hoop Stress location 4 -2.725 22.718 7.573Hoop Stress location 5 -1.110 1.754 0.585Hoop Stress location 6 -1.785 2.839 0.946

PDS simulation Results Response SurfaceNominal Tolerance STDEV

Hoop Stress location 1 0.344 42.825 14.275Hoop Stress location 2 -1.008 36.561 12.187Hoop Stress location 3 -0.742 29.981 9.994Hoop Stress location 4 -2.755 23.389 7.796Hoop Stress location 3 -1.113 1.850 0.617Hoop Stress location 4 -1.786 2.991 0.997



Static t=0

32.1

.CC

cr tC σε =

Primary Creep Eqn Time Hardening:-

• Applicability of Time hardening equation?

• Availability of Viscoelastic models and data?

• We need “push button tools” but appropriate science behind them

Deflection of R1 over time



• Stress state changes over time.

• How does this influence failure rate of system?

• Time hardening?


Case study 2:- Further Work

• Carry out PDS creep simulation

• Implement Viscoelastic models.

• Develop more robust Stress relaxation/creep simulation methods.

• Develop experimental verification test apparatus.


Summary of Presentation

• CAE strategy needs careful consideration to ensure effective use and maximum return.

• Experimental validation is just as important as the simulation itself.

• Tools best used as comparative ones when confidence has been gained.

• Developing high performance plastic components is not easy.

• Highly advanced simulations can be resource intensive.

Key Messages:-


AcknowledgementsAstraZeneca

G Dean, L Crocker and R Mera NPL

Bryan Deacon, Ticona UK Ltd.

Ansys, Sheldon Imaoka


References:-• “Design optimisation of an electro scalpel”,Ansys solutions V1 No. 2

1999• “Redesign of a medical stent “:- Ansys solutions V1 No. 2 1999• “Quality based design with probabilistic methods” Dr. Stefan Reh,

Dr. Paul Lethbridge, Dale Ostergaard, Ansys Solutions Volume 2 Number 2.Spring 2000

• “ The Ansys probablistic design system”, Dr. Stefan Reh, Volume 3 number 1

• “Easier ways to make a packet”,Professional Engineering 24 Feb 1999.

• “The macroscopic yield behaviour of polymers” Ram Raghava, Robert M Caddell, Gregory S. Y. Yeh. J Material Science 8 1973 225-232

• Observations on the creep of two material structures . T.Hyde et al Journal of Strain Analysis, V 31 No. 6 Nov 1996.

• Sheldon Imaoka, www.ansys.net/ansys

application of computational techniques during the development of packaging and medical devices

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