digital twin: mitigating unpredictable, undesirable ......digital twin model. 6. data. information....

Virtually Perfect: Driving Innovative and Lean Products

Copyright 2003-2015, Michael W. Grieves, LLC

The image part with relationship ID rId26 was not found in the file.

Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in

Complex Systems

Dr. Michael GrievesFlorida Institute of Technology

Center for Advanced Manufacturing and Innovative Design (CAMID)




AIAA – CASEComplex Aerospace Systems Exchange




Problem with Metaphors

• Glosses over substantive differences between source and target domains

• Hinders work on detail execution issues• Leads to misleading assumptions• Assumes that everyone has same

cognitive framework




Categories of System BehaviorSystem Behavior

Predicted Behavior Unpredicted Behavior

PredictedDesirable

(PD)

UnpredictedDesirable

(UD)

PredictedUndesirable

(UU)

UnpredictedUndesirable

(UU)

Figure 1




Creation

Product Lifecycle – 4 PhasesProduction

Disposal

Operations

Figure 2




Digital Twin Model

6

Data

Information

100100100111010110110001110001010001011011111001100101110100000000111010110001000111010101000001001000010100011110000111000101000001010000010000010100001010101010101100011010101110000110010101011100011000011111000001110010101010000100000101000000001000000100000001100000111000001111000111000000111100001010110000111100010101000000011111100001101010000011100001110011110000101010101000000111000011000010001101110100001001000010101000010101011110001111000001010110011110010100001110000110000100000010000000000111000011100000111111100011010101000010000011111001101010101000110111010000100001000001001011001000000100000001111100000010000001110000100001001000001000011100111100001000011100101010110011000010000011111100101010101101Virtual SpacePhysical Space

Digital Thread

Digital TwinEnvironment




Per

ProductEngineering

ManufacturingEngineering

Manufacturing

Operations

ConceptEngineering

RequirementsDecommissioning

100100100111010110110001110001010001011011111001100101110100000000111010110001000111010101000001001000010100011110000111000101000001010000010000010100001010101010101100011010101110000110010101011100011000011111000001110010101010000100000101000000001000000100000001100000111000001111000111000000111100001010110000111100010101000000011111100001101010000011100001110011110000101010101000000111000011000010001101110100001001000010101000010101011110001111000001010110011110010100001110000110000100000010000000000111000011100000111111100011010101000010000011111001101010101000110111010000100001000001001011001000000100000001111100000010000001110000100001001000001000011100111100001000011100101010110011000010000011111100101010101101

Digital Twin Through the Lifecycle

Perceived Value




ProductEngineering

ManufacturingEngineering

Manufacturing

Operations

ConceptEngineering

RequirementsDecommissioning

100100100111010110110001110001010001011011111001100101110100000000111010110001000111010101000001001000010100011110000111000101000001010000010000010100001010101010101100011010101110000110010101011100011000011111000001110010101010000100000101000000001000000100000001100000111000001111000111000000111100001010110000111100010101000000011111100001101010000011100001110011110000101010101000000111000011000010001101110100001001000010101000010101011110001111000001010110011110010100001110000110000100000010000000000111000011100000111111100011010101000010000011111001101010101000110111010000100001000001001011001000000100000001111100000010000001110000100001001000001000011100111100001000011100101010110011000010000011111100101010101101

Digital Twin Through the Lifecycle




Digital Twin Types

• Digital Twin Prototype (DTP)

• Digital Twin Instance (DTI)

• Digital Twin Aggregate (DTA)

Prognostics

Interrogative




System Engineering Models

Figure 4




Digital Twin Implementation Model

Figure 5




Real vs. Virtual Costs

Figure 6




Obstacles

• Organizational siloing / culture• Physical world knowledge• Magnitude of possible system states




Possibilities

• Usage feedback to design• System front-running



The image part with relationship ID rId26 was not found in the file. Net Shap…Mat

erial

Size

Additive Manufacturing

15

Can we make it?

Should we make it?

2 questions…

“The ultimate vision for the digital twin is to create, test and build our equipment in a virtual environment. Only when we get it to where it performs to our requirements do we physically manufacture it. – John Vickers, NASA Principal Technologist, The Economist




ExteriorWindow sBumpersFasciaGrilleTrimMirrorsHandlesFuel Filler Door ?HeadlampsTail lampsWipers…

InfotainmentHead unitDisplaysAntennasSwitches…

Electrical ComponentsWiringLocking system…

ToolsJigs Fixtures

…Stamping Dies

Die trim steel ?…

Molds…

Demonstrator Software System

Bill of Material, Bill of Equipment

, Materials, Machine/Material CapabilitiesMachine

Define Requirements

in tabular format

Analyze Physical Criteria

Analyze Financial Criteria

S

A

M

C/T

MachinesIDManufacturerModelModePower (W)AM ProcessGeneral Material TypeSpecific Material TypeX (in)Y (in)Z (in)Price (USD)Operation Cost_$/hrMaintenance Contract_$/yr

MaterialsIDcompanyMaterialTypePrice $ per kgShore ScaleHardnessUTS_MPaTensile Mod_MPaElongation to failure_%Flexural Strength_MPa

Machine-Material Systems

IDMachine1MaterialMin Hole dia_mmMin Wall Thickness_mmMin Column X-section_mmMin Groove Width_mmAngled Surface Min Ra_umDeposition Rate_kg/hrMin UTS_MPaMin Tensile Modulus_MPaMin Elongation to Break_%Min Flexural Strength_MPaAbsolute Accuracy mm/mExpected IterationsMaterial Reuse_%

PartsIDPart NameSTL fileHeight_mmWidth_mmLength_mmMaterial Volume_mm 3̂Min wall thickness_mmMin hole dia_mmMin Column X-section_mmMin Groove Width_mmPrototype divisible_(y/n)Prototype MaterialPrototype Load TypePrototype FinishPrototype Tolerance_mm/mPrototype QtyPrototype Target PricePrototype Target Lead TimeEnd Use divisible_(y/n)End Use MaterialEnd Use Load TypeEnd Use FinishEnd Use Tolerance_mm/mEnd Use QtyEnd Use Target PriceEnd Use Target Lead TimeEnd Use Hardness Shore ScaleEnd Use HardnessEnd Use UTS_MPaEnd Use Tensile Modulus_MPaEnd Use Elongation to Failure_%End Use Flexural Strength_MPa

• Surface finish

• Distortion• Geometric

Limitations

Input (BOM, BOE)

Data Stora

ge

Eventually –auto extract from Teamcenterdatabase

(Machine Data is expected to be populated and

maintained externally)

• Build cast-finish PT part

• Build sand mold for PT part

• Build pattern for ceramic mold

• Build insert for injection mold

• Build trim handle for shift knob

• …

SIZE

ACCURACY/NET SHAPE

MATERIAL

CONVENTIONAL (NON-AM)

PROCESSES

Pattern build

Sand

Core

build

Cast

Machine

Pattern build

Sand

Core

build

Cast

Machine

Direct metal print

Indirect sand core print

Option 1

Option 23

Die trim steel

Prototype Intake manifold

Saw Aluminum block

Rough

machine banks, port

s

Finish

machining

Cleanin

g

Weld

runners

ORANGE = Potential portion to be replaced

Direct metal print

FuelFillerDoor

Design & Build Mold

Injection Mold Paint

Direct polymer printing

What do we want AM to deliver?

Yellow = Missed by < 10% volume

QUANTIFYING THE ROLE OF PART DESIGN COMPLEXITY IN USING 3D SANDPRINTING FOR MOLDS AND CORES, 2016 Conner et. al.

Example –Breakeven Analysis for Sand Molds:Costs per sand mold, varying quantities &

Analysis

Interactive Analysis

Output

Database filters to begin for Demo

Can We / Should We …..deteriorates as we move to production?

one part at a time

+ Geometry

Files

http://i.istockimg.com/file_thumbview_approve/18336484/6/stock-illustration-18336484-car-assembly-and-parts-black-white-vector-icon-set.jpg

PARTS AND TOOLS

Automatic analysis results of multiple parts & tools.

Vehicle PartsEngine & Transmission Parts

Cylinder HeadBlockCamshaftCrankshaftIntake Manifold ?…

ChassisSteeringSuspensionTiresWheels…

Heating, Ventilation, and ACDuctsVents…

InteriorDash panelsInstrumentsSeatsTrimLightsSpeakersSafety BeltsAirbags…

BodyStructureClosuresSeals…

2

1

1

32

Analysis performed one at a time

COST /THROUGHPUT

RESULT

RESULT

RESULT

RESULT

2016-05-26 MAS

Additive Manufacturing SAM-CT Framework




AM DB Architecture

Add ons:ImagesFor fee add ons:BrochuresReferralsDemonstration videosTraining videos




Questions/[email protected]

digital twin: mitigating unpredictable, undesirable ......digital twin model. 6. data. information....

Documents