simulation process in packaging

8/9/2019 Simulation Process in Packaging

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ACHIEVING THE OPTIMAL BOTTLE DESIGN BY VIRTUAL

SIMULATION TECHNIQUES CASE STUDIES IN CONSUMERPACKAGED GOODS APPLICATIONS

Joanne R. ZuzelskiAltair Engineering, Inc.

0.0 ABSTRACT

Getting the best product to production and into the market quickly and efficiently requires an

integrated approach to packaging design. Traditional trial-and-error methods in packagedevelopment leave little opportunity to study and prove-out a variety of design alternatives without

impacting program cost and timing. Design optimization and advanced computer simulation can

be used to deliver critical insight into areas such as optimum package shape, package

performance, material selection, and material reduction with minimum investment yet yielding

significant returns. Collaboration between marketing, product design, manufacturing and key

contributors to product development is vital to ensure all requirements of the package design are

addressed in the process from aesthetics and consumer needs to manufacturing and

transportation. Employing the best design strategies and advanced technologies resulted in

innovation, cost reduction and design improvements leading to millions of dollars in annualsavings for two companies.

1.0 INTRODUCTION

Throughout automotive, aerospace, biomedical, consumer, and numerous other industries,

companies are striving to cut development costs, reduce time-to-market, and improve their

competitive edge in todays market. Since the mid 1970s, computer-aided methods including

CAE have increasingly played an important part in the product development process. Significant

advancements in CAE technology allow product developers to visualize a concept and perform

highly complex loading in a 3-D virtual environment. Using finite element techniques to

analytically represent the part and then simulate load and constraint conditions, designers and

engineers are able to predict the performance of a concept design in advance of any investment

in tooling, prototyping, or physical test validation. What-if studies can also be conducted to

determine the effect of varying geometric features, material thickness, material properties, and/or

alternative load cases. This allows manufacturers to have greater confidence that the best


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solution has been achieved before initiating a physical validation phase. Up-front CAE methods

include optimizing the product for design features, weight, performance, and/or material. Thebenefits of using CAE methods early in the design process include shorter development cycles,

reduced prototype and testing costs, and improved product attributes.

With the increased integration of simulation methods into product design has come the

advancement of CAE technologies such as optimization and process automation. Optimization

technologies allow the engineer to define a range of acceptable design parameters, and

performance and mass targets and analytically iterate to identify the optimum combination of the

variables to meet the objectives. Often times, tasks and processes of a repeatable, quantifiable

nature (such as creating the analytical model, applying typical loads, generating electronicreports, and so on) can be easily automated using software, thereby improving the productivity

and quality of the simulations.

The key to effective application of CAE lies in the ability to analytically represent the product and

its environment including geometry, material properties, expected loads, and constraints. With

consumer packaged goods, these requirements must also be balanced against more subjective

criteria such as package aesthetics and volume efficiency. The following case studies

demonstrate the process and benefits of applying an advanced analytical approach for the

development of two consumer packaged goods - a Lever Faberg bottle and a trigger spraybottle. Altair HyperMesh was used for finite element pre and post-processing in each of the

studies. Altair OptiStruct was used for shape and size optimization of the trigger bottle, Altair

StudyWizardfor automating a DoE (design of experiments) and optimization study on the Lever

pack, and additional commercially available finite element solvers were used for linear and non-

linear analyses.


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Figure 1: Bottle Design

2.0 CASE STUDIES IN PACKAGING APPLICATIONS

2.1 Lever Faberg Bottle

Consumer product packaging designers are faced with conflicting requirements throughout the

development process. Good package aesthetics are vital for the success of the product, while

unit costs must be minimized and suitability for stacking and transportation maintained. A

significant improvement in the design process can be gained if design information can be clearly

communicated to the product designers early in the design process. This case study

demonstrates how design optimization and advanced CAE can be used to deliver this. The

resulting design process facilitates the early definition of an attractive package shape (Figure 1)incorporating features that will meet the structural and cost requirements.

The design optimization process requires input in the form of a series of alternative shapes for the

package, definition of a design objective (cost or weight) and constraints (structural,

manufacturing). An automated series of structural assessments are then performed, and design

sensitivity information and an optimum shape defined.

The design tools primary objective is to facilitate provision of clear information to the product

design team about how to choose a shape which will be economical to produce, manufacturable,

and capable of withstanding the required loads. It is extremely important that this information is

generated in a timely manner. Automation of the process is therefore required wherever

possible.


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The design process is comprised of five major components. The first is the model generationprocess that includes importing CAD (computer-aided design) data, automated CAD cleanup and

automated finite element meshing. The deliverable from this step is a baseline simulation model

of the package as shown in the series of model generation pictures in Figure 2.

Figure 2: Model Generation Process

The next step, the parameterization process, uses advanced morphing technology to apply

complex changes to 3D (3-dimensional) shapes, and thickness linking to allow weighted variation

through the height of the bottle (size and shape variables are shown in Figure 3). The shape

variables can be reviewed interactively and animated in 3D, making this complex information

easily accessible to all members of the design team. The definition of the shape variables

requires input from the design team to ensure that the look and feel of the pack is maintained and

that none of the variables will cause manufacturing problems.


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Figure 3: Shape Variables


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Size variables are defined based on knowledge of the manufacturing process. Control of the

parison wall thickness in practice is limited to changing wall thickness in horizontal bands downthe major axis. Design variables were therefore defined to parameterize wall thickness of the

pack in achievable bands (Figure 4). At the end of the parameterization process, a series of

design geometry variations are available for use in the optimization phase.

Figure 4: Definition of Size (Wall Thickness) Variables

The simulation environment for this application incorporates advanced solver technology to

capture the non-linear collapse of the pack under the enveloping design condition (top load). This

provides a means of understanding in a very short time frame (less than 1 hour) how the

proposed geometry will perform, without the need for physical prototyping and testing. Before

proceeding with the design optimization process, testing and verification of the simulation

procedure on the baseline design is necessary. The top loading surface is moved vertically

downwards to capture the peak buckling load and the post buckling behavior of the pack. Results

from the simulation are presented in Figure 5.


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The peak load capacity of the pack is influenced by the geometry, the redistribution of load due to

contact with the loading platen and base plate and the non-linear material properties.

The collapse response under top loading (Figure 5) demonstrates that buckling first occurs in the

neck region (figure on left). Load is then re-established before the base of the bottle buckles

(figure on right). A comparison of the predicted response with actual test data revealed that the

simulation environment accurately captures the real world response.

Figure 5: Stress contour plot of the baseline bottle during collapse simulation

Optimization is a two-stage process, which uses as input the baseline model and shape variables

plus additional specification of the optimization objective and constraints. To start the process a

Design of Experiments (DoE) study is performed. This yields a summary of the sensitivity of the

design performance to the shape changes. This is followed by a full non-linear optimization to

define the optimum shape. The whole process is set up and controlled from StudyWizard, which

automates the procedure and simplifies user input.

The first step in the DoE study is a screening exercise to reduce the number of design variables.

The design variables, which have least effect on the objective and constraints, are removed to

leave the key design variables for the optimization phase.


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The screening step identifies that the buckling capacity of the bottle is most influenced by the

thickness at the top of the bottle. Significant sensitivity is also noted for global and local shapechanges including: shoulder slope, footprint size and neck sculpting. A review of the data also

indicated how the upper and lower bounds of the design variables affect the response. For

example, increasing the lip thickness variable increases the buckling capacity, whereas

increasing the sculpt depth at the neck has the opposite effect.Seven design variables were selected from the DoE studies (main effects results shown in Figure

6) and were taken forward into the optimization phase.

Figure 6: Main Effects on Buckling Capacity from DoE Studies

The design optimization procedure finds an optimum combination of design variables to meet the

objective (minimize mass) while satisfying the constraints (buckling capacity). The optimization

generated a bottle design with the parameters summarized in Table 1. It is clearly demonstrated

that design optimization can automatically provide the right mix of design parameters to save

weight and increase performance.


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Variable Value (mm)Base Thickness (t1)Shoulder Thickness (t9)Lip Thickness (t11)Neck Sculpt (Shape Variable 2)Footprint Size (Shape Variable 3)Label Recess (Shape Variable 5)Shoulder Slope(Shape Variable 7)

1.00.81.1

-0.6 (Pulled out)0.0

-0.3 (Pulled out)-3.0 (Shallower)

Response Value

Mass

Buckling Capacity

212g (from 223g, reduction of 5%)

490N (from 420N, increase of 20%)

Table 1: Summary of Optimum Design

2.2 Trigger Spray Bottle

The objective of this project was to demonstrate the application of virtual engineering tools to

redesign a trigger bottle for a leading manufacturer of household cleaning products and achieve

cost savings and improved structural performance. The project involved the integrated use of

concept design methods and industrial design to meet three key objectives increase thenumber of bottles/pallet, reduce packaging material, and reduce bottle mass. Application of

optimization methods was employed to identify a better package design in less time than using

traditional design approaches. Design and packaging criteria for the new design included:

bottle design space

performance (under top load, fill, and drop loads)

label area

load case selection

bottles per box

stack height

board selection

bottle orientation

box size


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Top load, bulge (fill load) and drop simulations were conducted on the current 32-ounce trigger

bottle design to establish the baseline performance. Figure 7 shows the stress contour on thedeflection profile of the current bottle under a top load alongside a graph of force versus time.

Figures 8 and 9 below show the stress contour and the deflection profile of the current bottle

under a bulge load and drop load, respectively.

Figure 7: Stress contour and deflection under top load

Figure 8: Bulge Load Figure 9: Drop Load


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A footprint study was then conducted on five selected footprints with costant height but varying

length and width. These footprints were rank ordered subjectively based on factors such asbottle stability, number of bottles per pallet, stack pattern and design efficiency. Concurrent to

this study, concept sketches were developed for eight designs as shown in Figure 10.

These sketches were subjectively evaluated and rank ordered by a multi-disciplinary team based

on factors such as volume efficiency, label area, aesthetics, grip shape, top load ability, and

manufacturing. The top ranked design concept RADIUS was selected for development of a 3D

model (see Figure 11), using the optimal footprint resulting from the rank ordered study. The

model was then used to optimize the structural performance.

Figure 10: Concept sketches


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Shape and size (gauge) optimization was performed to optimize the bottle for top load resistance,

bulge, and drop events while minimizing gram weight of the bottle. Figure 12 shows the location

of the shape variables at the shoulder (shape variable 1) and at the wall fillet (shape variable 2),

along with horizontal bands of wall thickness allowed to vary in the gauge optimization.

The optimized design detailed in Figure 13 resulted in passing bulge and drop test requirements

with a 2% decrease in the weight of the (32 ounce capacity) bottle, and a 66% increase in the top

load capacity as shown in Figure 14.

Figure 11: Development of 3D model

Figure 12: Shape and size variables


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The use of virtual product development methods followed in this project allow for the development

of a new, optimized bottle design, such as the bottle done for this project and illustrated in Figure

15, in approximately 4-6 weeks. In addition to faster time-to-market, substantial cost savings

totalling over $2M in the first year of production have been realized as a result of several

advantages of the new design:

Figure 13: Optimum design

Figure 14: Results of top load


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Lower shipping costs due to optimized footprint and increased number of bottles/pallet

Lower packaging corrugate costs due to increase strength of top load capacity Reduced resin costs due to weight savings of optimized bottle

Blow molding savings due to new bottle shape allowing higher production volumes per cavity

tool

3.0 Conclusions

A clear need and interest has been identified in the packaging industry for reliable design input

early in the development process. The requirements for successful packaging design are many

and conflicting, but the package must always remain attractive to the consumer.

The design technologies and process, applied to these two consumer goods package designs,

can help bridge the gap between those involved in defining the right look and feel for the product

and those involved in engineering the best solution. In both cases, the optimization results

generated a reduced mass design concept given a baseline example design already on the

market as a starting point. A first pass design optimization yielded a 2-5% reduction in bottle

Figure 15: New trigger bottle design


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masses, while exceeding the top-load capacity requirement and achieving further savings in

manufacturing and transportation.

The high level of automation in the process facilitates rapid delivery of the design information.

Advanced visualization tools and intuitive user interfaces make this information highly accessible

to all of the design team.

The marketing team and product designers can get timely information about how to maintain or

improve the appearance of the pack without compromising manufacturing or transportation

performance. Careful choice of shape changes for the pack becomes a collaborative process

between structural, manufacturing and product designers, with the design tool providingindependent review. A by-product of the optimization process is readily accessible design

sensitivity information, clearly indicating which shapes are beneficial to the pack performance.

Extraction of the best geometry, which meets the requirements of all of the team is the final step

in the process and the resulting CAD data can be taken forward for prototyping.

This design process provides potential for reducing design cycle times, through facilitating

definition of strong design concepts early in the design process, which require fewer down-stream

modifications. Close team collaboration is a requirement: design, marketing, manufacturing and

engineering professionals are all called upon to review the proposed shape changes and

understand their impact on the design.

4.0 References

The case study discussed herein on the Lever Faberg bottle and the summary were derived

from a detailed technical paper entitled An Advanced Method for Optimizing Packaging Design

by co-authors R. McNabb of Lever Faberg, and Martin Kemp and Steve McMahon of Altair

Engineering, Ltd and first presented in November 2002 at the Altair HyperWorks Conference

2002 in Coventry, UK.

Introductory portions of this paper, including the case study on the trigger bottle were first

presented by the author at the Rapid-Pack 2003 conference sponsored by Schotland Business

Research, Inc, on September 15, 2003 in Frankfurt, Germany in the paper titled Simulation-

Driven Product Design to Accelerate the Package Development Process.

simulation process in packaging

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