Simulation and Modelling Collaboration with PLM Kenneth J Rasche, P.E. Senior Engineering Manager Whirlpool Corp.

(1)

Simulation and Modelling

Collaboration with PLM

Kenneth J Rasche, P.E.

Senior Engineering Manager – Whirlpool Corp

.

(2)

Projects Create Data Sometimes SMEs Create Info Sometimes SMEs Create Knowledge

Team rarely is able to reuse knowledge

Rarely have the right data and info to create

reusable knowledge

PROJECT PROCESS

Don’t wait and see if we can and/or do create knowledge :

Drive Reusable Knowledge with the design process !

Executive Summary

Organizational Knowledge Framework

BEFORE

SMEs pre-defineProject Deliverables to update and

create reusable knowledge chunks Organizational Knowledge Framework AFTER Project Needs viewed through knowledge

COLLABORATIVE PROCESS

Update

and

Create

Knowledge Gaps for this specific project

Reuse

This part of the process is usually not consistent

(3)

Executive Summary

Organizational Collaboration

of simulation and modeling offers huge competitive advantages by

allowing an organization to move closer to “knowledge Nirvana” where:

No knowledge has to be learned more than once

The organization only learns what it needs to learn

PLM systems offer capabilities to enable an organizational collaboration process, but some lower level

processes have to be in place to maximize the benefit

–

Product Knowledge Structure

–

Reusable Knowledge

–

Surrogate Models

(4)

Content

•

Context - Whirlpool

•

Reusable Knowledge

•

Surrogate Models

•

Design for Population

•

Organizational Collaboration in PLM

(5)

A very successful 100+ years

$2.3M

(1942)

$1.1B

(1970)

$3.4B

(1985)

$8.2B

(1995)

~$20B

(Current)

Sales

$14B

(2005)

Context - Whirlpool

(6)

Main Product Lines

•Side X Side •Top Mount •Bot Mount •BIR SxS •BIR Bot Mount •CD Side X Side •CD Bot Mount •Refrig. Drawers

Laundry

Refrigeration

Cooking

Cleaning

Small Appliances

•Vertical Axis •Horizontal Axis •Dryers •Pedestals • Accessories •Fabric refreshers •Cook Top •Range •Built In •Slide in •Microwaves •Freestanding •Warming drawers •Tall Tub •Stainless Tub •Plastic tub •Water products •Trash compactors •Disposers •Stand Mixer •Toasters •Blenders •Food processor •Coffee maker •Immersion blenders •Coffee grinders

Context - Whirlpool

(7)

Global Footprint

NORTH AMERICA REGION

•

~30,000 Employees

•

7 Technical Centers

•

12 Factories

EUROPE REGION

•

~14,000 Employees

•

5 Technical Centers

•

10 Factories

LATIN AMERICA REGION

•

~21,000 Employees

•

3 Technical Centers

•

3 Factories

ASIA REGION

•

~5,000 Employees

•

5 Technical Centers

•

5 Factories

GLOBALLY

9+ Brands

~30 factories

20 Technical Centers

10,000+ SKU’s

120+ Product platforms

100,000+ parts

5,000 Technical People

Numerous organizational borders across businesses, regions,

functions, product categories, and locations

(8)

Like Dewey Decimal System, product

knowledge structure shows us where the

knowledge belongs

•

Product Knowledge Structure needs to be defined

to prevent gaps and overlaps

•

“Good search tool” is not the answer

– A place for everything, and everything in its place

•

Provides “map” to where knowledge is and where it

should go

–

Structure has 2 axes:

• Product – Reusable pieces of the product

• Attributes – System models of product performance

•

Provides all teams with a “live” place for

collaboration

(9)

Attribute Interfaces Ge o m e tri c In te rf a ce s

9 A tt ri b u te s, su b -a tt ri b u te s a n d f u n ctio n s

Reliability – Door Alignment

H ing e -B ott om

Engineering Sub-assemblies (sub-systems)

Knowledge Chunk

“Soft” sub-attribute requirements and collaboration allow for greater innovation and system optimization

(10)

Reusable Product Knowledge Chunk

10

Application Input

•Force • CFM • Temperature • Delta Pressure • Displacement • Wattage • etc.

Attribute Output

•Force • CFM • Temperature • Delta Pressure • Displacement • Wattage • etc. Design B Design A A p p lica tio n I n p u t 1 P a ss Fa il

• “Point data” is typically not very reusable, we need to “connect the

dots”

• “Level 1” reusability is evaluating the design over a range of foreseeable system inputs

• “Level 2” reusability is evaluating the design conceptover a range of

foreseeable system inputs and

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Reusable Product Knowledge Chunk

11 Concept B1 Design A A p p lica tio n I n p u t 1 A p p lica tio n I n p u t 2 Pass Fail Concept B2

Knowledge Chunk: Initial version of the knowledge chunk shows clearly that Design A passes and by what margin – knowledge is reusable and provides format for additional design development

New Application: New application is added and clearly shows Design A will not work for this new application – we need a new design

Concept B1: First concept is added

and performance is clearly not enough improvement

Concept B2: The concept is iterated

and B2 clearly shows there is

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Reusable Product Knowledge Chunk

12 0.0 0.5 1.0 1.5 2.0 2.5 0.00 200.00 400.00 600.00 800.00 1000.00 Ver tical Defl e ction , m m Vertical Load AK3X_69mm

AK3X - Given food Load AK3X_64mm

New Design Target

N e w D e s ign L o a d Old D e s ign L o a d

Old Design Target

Pros

• Automate simulations upfront – so additional work is mostly compute time, not human time

• If new designs are in inference space, the model does not have to be run again – the knowledge chunk already has the answer

• Better development of understanding how the design performs – helps ensure models are representative of physical parts and application • Correlation and Calibration can be built up over multiple applications – avoid chasing the latest set of test results

Cons

• Pressure to “just do the minimum for my project” • Standardization can impede innovation

(13)

Surrogate Models

13

System Models

(Model of system level performance)

Pro’s

•Interactions • Fast

• Optimize system configuration • Develop control logic

Con’s

• +/- 5% to 10%

• can’t evaluate many design changes • miss cost or performance opportunities

Pro’s

•Can evaluate all design changes • Optimize performance vs cost

Con’s

•Slow

• Sub-optimization risk

Detailed Models

(FEA, CFD, captures geometry)

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Surrogate Models

Clearly define interfaces between all models

•

Characterize on the variables that drive the

system design

•

Cascade inputs and outputs up and down the

system of models

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Surrogate Models

15

System of Models (initial rev)

Basic equations characterizations

System of Models (next rev)

Characterizations replace basic equations

Improved characterizations

•

Characterize sub-assembly models

to create surrogate models

•

Characterize around the right

variables

•

Make the surrogate model as

re-usable as possible –

Don’t skip levels

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Surrogate Models

16

•

Multi-dimensional

•

Data sets to capture the

complete inference

space

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17

Spec. limit

Occurren

ces

System Population Performance

X%

Population Value

Nominal

Test 1 Sample Test 2 Sample

•

Variation of system level

performance from sample test to

sample test makes it difficult to

correlate and calibrate

•

Structuring the problem around

the knowledge chunks allows us

to understand the variation of

each piece of the product and

how it contributes to the overall

performance

(18)

18 Hinge Stiffness Door Stiffness Cabinet Square Hinge Location Cabinet Stiffness

Door Alignment = f(Cab Square, Door Stiff, Hinge Stiff, Hinge Loc, Cab Stiff, etc.)

•

Evaluating the knowledge chunks

individually allows you to remove

much

of the variation

•

Monte Carlo simulations are

required since very few of the

distributions are normal

(19)

System Model

Str002

Thm001

Perf004

Mat003

System Model

1 9

Structure

Team

Str001

Str002

Str003

Moldflow

Team

Thm001

Thm002

CFD

Team

Perf001

Perf003

Perf004

Project

Manufacturin g

Mat003

PLM enables sharing

• Shares/reuses models with multiple deliverables/project and product teams • Creates and clones their own models

• Revisions to models are reflected immediately everywhere they are shared

• Enables driving product knowledge chunk

development through projects

Mat003

Thm002

Str001

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20

$10

$13

$22

System Requirement

$11

$13

$18

Collaboration with PLM

$45 $42 = $3 savings

S o ft su b -sy ste m Re q u ir e m e n ts Ri g id su b -sy ste m Req u ir e m e n ts

Cost functions are not linear

• Achieving the last 2% of a sub-system requirement may cost $4 • Exceeding a different sub-system requirement may only cost $1

• Sub-system designs migrate towards cost inflection points in optimum designs

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Projects Create Data Sometimes SMEs Create Info Sometimes SMEs Create Knowledge

Team rarely is able to reuse knowledge

Rarely have the right data and info to create

reusable knowledge

PROJECT PROCESS

•

Typical “project” design process generates lots of data…….

–

Teams are often off to the next project before much/any of the learnings are captured

–

May or may not be able to distill or format into reusable information and/or knowledge

–

New project teams often don’t know where to find existing knowledge

–

New project teams don’t trust the existing knowledge

This part of the process is usually not consistent

(22)

Organizational Knowledge Framework

BEFORE

SMEs pre-defineProject Deliverables to update and

create reusable knowledge chunks Organizational Knowledge Framework AFTER Project Needs viewed through knowledge

COLLABORATIVE PROCESS

Update

and

Create

Knowledge Gap for this specific project

Reuse

•

PLM database capabilities allow functional teams and project teams to share information “real time”

– Information and knowledge sharing does not have to wait until it is “published”

•

Definition of knowledge chunks allows functional teams to share information with several teams because

it is reusable

– Information/knowledge is not system application specific

– Information/knowledge can be reused in the future

•

Knowledge structure definition allows predefining project deliverables to be reusable knowledge

– Don’t “wait and see” if we can create reusable knowledge after the project is complete

– Allows multiple teams to share the knowledge as it is created

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Conclusions

23

Organizational Collaboration

of simulation and modeling offers huge competitive advantages

by allowing an organization to move closer to “knowledge Nirvana” where:

•

No knowledge has to be learned more than once

•

The organization only learns what it needs to learn

PLM systems offer capabilities to enable an organizational collaboration process, but some

lower level processes have to be in place to maximize the benefit

•

Reusable Knowledge

•

Surrogate Models

•

A tt ri b u te s, su b -a tt ri b u te s a n d fu n ctio n s

Reliability – Door Alignment

H ing e -B ott om

(24)

•

Thank You!